diff --git a/README.md b/README.md
index 04e3532..911f5c9 100644
--- a/README.md
+++ b/README.md
@@ -1,43 +1,128 @@
-# End-to-End Machine Learning Project
+# 📘 End-to-End Insurance Risk Analytics & Predictive Modeling
 
-This project is an end-to-end machine learning pipeline designed with a
-modular folder structure. It includes data processing, feature
-engineering, EDA, model building, reporting, and CI setup.
+A complete, modular, production-ready machine learning pipeline for
+insurance analytics.
 
-## Project Structure
+---
 
-    end-to-end/
-    .github\workflows
-    │   |──ci,yml
-    |   |──codeql.yml
+## Project Overview
+
+This project implements a **fully modular end-to-end ML pipeline** for
+insurance risk analytics and predictive modeling. It supports real-world
+insurance business applications such as:
+
+- Analyzing historical policies, claims, and exposures\
+- Performing EDA and anomaly detection\
+- Conducting hypothesis tests to validate key risk drivers\
+- Building models for claim probability, claim severity, and premium
+  optimization\
+- End-to-end reproducible ML pipeline with CI/CD support\
+- Integrated reporting, logging, and versioning
+
+---
+
+## Business Objective
+
+**AlphaCare Insurance Solutions (ACIS)** aims to:
+
+- Identify **low-risk customer segments**\
+- Optimize **premium pricing** while maximizing profitability\
+- Understand **factors contributing to claims**\
+- Support **actuarial and underwriting decisions**\
+- Enhance customer retention with targeted strategies
+
+---
+
+## Full Project Folder Structure
+
+    End-to-End-Insurance-Risk-Analytics-Predictive-Modeling/
+    ├── .github/
+    │   └── workflows/                 # CI/CD pipelines (tests, linting, dvc)
+    ├── configs/
+    │   ├── data.yaml                  # Dataset configuration
+    │   ├── dvc_remote.yaml            # DVC remote configuration
+    │   ├── logs.yaml                  # Logging settings
+    │   └── modeling.yaml              # ML model configurations
     ├── data/
-    │   ├── raw/               # Original data (untouched)
-    │   └── processed/         # Cleaned, transformed data
-    │
+    │   ├── raw/                       # Original data
+    │   ├── processed/                 # Cleaned & feature engineered data
+    ├── docs/                          # Documentation & reports
     ├── notebooks/
-    │   ├── 01_EDA.ipynb       # Exploratory Data Analysis
-    │   └── 02_Modeling.ipynb  # Model training & evaluation
-    │
+    │   ├── analysis/
+    │   │   ├── hypothesis_tests.ipynb
+    │   │   └── model_building.ipynb
+    │   └── exploration/
+    │       ├── data_overview.ipynb
+    │       └── eda.ipynb
+    ├── scripts/
+    │   ├── __init__.py
+    │   ├── clean_data.py
+    │   ├── run_eda_pipeline.py
+    │   ├── run_hypothesis_tests.py
+    │   └── train_models.py
     ├── src/
-    │   ├── eda/
-    │   │   └── eda_tools.py
-    │   ├── features/
-    │   │   └── build_features.py
-    │   ├── models/
-    │   │   └── train_model.py
-    │   └── utils/
-    │       └── data_loader.py
+    │   └── insurance_analytics/
+    │       ├── __init__.py
+    │       ├── core/
+    │       │   ├── __init__.py
+    │       │   ├── config.py
+    │       │   ├── logger.py
+    │       │   ├── registry.py
+    │       │   └── scheduler.py
+    │       ├── eda/
+    │       │   ├── __init__.py
+    │       │   ├── exploration.py
+    │       │   └── visualization.py
+    │       ├── models/
+    │       │   ├── __init__.py
+    │       │   ├── evaluation.py
+    │       │   ├── interpretability.py
+    │       │   ├── linear_regression.py
+    │       │   ├── random_forest.py
+    │       │   └── xgboost_model.py
+    │       ├── preprocessing/
+    │       │   ├── __init__.py
+    │       │   ├── cleaner.py
+    │       │   └── feature_engineering.py
+    │       ├── utils/
+    │       │   ├── __init__.py
+    │       │   ├── io_utils.py
+    │       │   ├── metrics.py
+    │       │   ├── project_root.py
+    │       │   ├── system.py
+    │       │   └── validation.py
+    │       └── viz/
+    │           ├── __init__.py
+    │           └── plots.py
+    ├── tests/
+    │   ├── integration/
+    │   │   ├── __init__.py
+    │   │   ├── test_dvc_integration.py
+    │   │   ├── test_eda_pipeline.py
+    │   │   ├── test_full_pipeline.py
+    │   │   └── test_model_pipeline.py
+    │   └── unit/
+    │       ├── __init__.py
+    │       ├── test_cleaners.py
+    │       ├── test_features.py
+    │       ├── test_hypothesis.py
+    │       ├── test_loaders.py
+    │       ├── test_models.py
+    │       └── test_registry.py
     ├── .gitignore
-    ├── requirements.txt
     ├── README.md
+    └── requirements.txt
+
+---
 
-## How to Run This Project
+## How to Run the Project
 
 ### Create a Virtual Environment
 
 ```bash
 python -m venv venv
-.env\Scriptsctivate
+venv\Scripts\activate        # Windows
+source venv/bin/activate     # macOS/Linux
 ```
 
 ### Install Dependencies
@@ -46,36 +131,58 @@ python -m venv venv
 pip install -r requirements.txt
 ```
 
-### Run Notebooks
+### Run Data Cleaning
 
 ```bash
-jupyter notebook
+python scripts/clean_data.py
 ```
 
-## Features
+### Run EDA Pipeline
 
-✔ Clean modular code\
-✔ Separate folders for EDA, features, models, utils\
-✔ Ready for CI/CD\
-✔ Clear data directory hierarchy\
-✔ Reproducible ML workflow
+```bash
+python scripts/run_eda_pipeline.py
+```
 
-## Requirements
+### Train Machine Learning Models
 
-Add libraries in `requirements.txt`, example:
+```bash
+python scripts/train_models.py
+```
 
-    numpy
-    pandas
-    scikit-learn
-    matplotlib
-    seaborn
-    jupyter
+### Use Jupyter Notebooks
+
+```bash
+jupyter notebook
+```
+
+---
+
+## Key Features
+
+- ✔ Modular ML architecture\
+- ✔ Clear data/configs/scripts separation\
+- ✔ DVC versioning\
+- ✔ CI-ready workflows\
+- ✔ Logging & validation utilities\
+- ✔ Interpretability (SHAP, feature importance)\
+- ✔ Reproducible experiments
+
+---
 
 ## Reports
 
-- `interim_report.md`: insights during project development\
-- `final_report.md`: final results, visualizations, and model
-  performance
+- `docs/interim_report.md`\
+- `docs/final_report.md`
+
+---
+
+## Testing
+
+```bash
+pytest
+```
+
+---
 
 ## Version Control
 
diff --git a/configs/data.yaml b/configs/data.yaml
new file mode 100644
index 0000000..236b922
--- /dev/null
+++ b/configs/data.yaml
@@ -0,0 +1,17 @@
+data:
+  data_dir: "data"
+  raw_dir: "data/raw"
+  processed_dir: "data/processed"
+
+logs:
+  logs_dir: "logs"
+
+reports:
+  reports_dir: "reports"
+  plots_dir: "reports/plots"
+
+models:
+  models_dir: "src/insurance_analytics/models"
+
+artifacts:
+  artifacts_dir: "artifacts"
diff --git a/notebooks/01_EDA.ipynb b/configs/dvc_remote.yaml
similarity index 100%
rename from notebooks/01_EDA.ipynb
rename to configs/dvc_remote.yaml
diff --git a/notebooks/02_Modeling.ipynb b/configs/logs.yaml
similarity index 100%
rename from notebooks/02_Modeling.ipynb
rename to configs/logs.yaml
diff --git a/reports/interim_report.md b/configs/modeling.yaml
similarity index 100%
rename from reports/interim_report.md
rename to configs/modeling.yaml
diff --git a/scripts/build_features.py b/notebooks/analysis/hypothesis_tests.ipynb
similarity index 100%
rename from scripts/build_features.py
rename to notebooks/analysis/hypothesis_tests.ipynb
diff --git a/scripts/evaluate.py b/notebooks/analysis/model_building.ipynb
similarity index 100%
rename from scripts/evaluate.py
rename to notebooks/analysis/model_building.ipynb
diff --git a/scripts/train.py b/notebooks/exploration/data_overview.ipynb
similarity index 100%
rename from scripts/train.py
rename to notebooks/exploration/data_overview.ipynb
diff --git a/notebooks/exploration/eda.ipynb b/notebooks/exploration/eda.ipynb
new file mode 100644
index 0000000..5a1c3a3
--- /dev/null
+++ b/notebooks/exploration/eda.ipynb
@@ -0,0 +1,1162 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "e33a3d17",
+   "metadata": {},
+   "source": [
+    "### Module Imports"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "13b2741d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import sys\n",
+    "from insurance_analytics.core.registry import settings\n",
+    "from insurance_analytics.preprocessing.cleaner import cleaning\n",
+    "from insurance_analytics.data.load_data import load_data\n",
+    "from insurance_analytics.preprocessing.feature_engineering import add_loss_ratio\n",
+    "from insurance_analytics.eda.exploration import (\n",
+    "    dataset_overview, duplicated_rows, detect_outliers_iqr,summarize_missing\n",
+    ")\n",
+    "from insurance_analytics.utils.io_utils import write_csv\n",
+    "from insurance_analytics.viz.plots import ( plot_histogram,\n",
+    "plot_bar,\n",
+    "correlation_matrix,\n",
+    "boxplot_outliers,\n",
+    "scatter_outliers,\n",
+    "highlight_outliers_iqr,\n",
+    "scatter_by_group)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4e3093f8",
+   "metadata": {},
+   "source": [
+    "### Config and Data Load"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "cc7d4732",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Data loaded successfully: 1000098 rows, 52 columns\n"
+     ]
+    }
+   ],
+   "source": [
+    "name = \"MachineLearningRating_v3.txt\"\n",
+    "raw_path = settings.DATA[\"raw_dir\"] / name\n",
+    "report_path = settings.REPORTS[\"reports_dir\"]\n",
+    "plots_dir = settings.REPORTS[\"plots_dir\"]\n",
+    "processed_dir = settings.DATA[\"processed_dir\"]\n",
+    "\n",
+    "# Load data with error handling\n",
+    "try:\n",
+    "    df = load_data(raw_path)\n",
+    "    print(f\"Data loaded successfully: {df.shape[0]} rows, {df.shape[1]} columns\")\n",
+    "except FileNotFoundError:\n",
+    "    print(f\"Error: File not found at {raw_path}\")\n",
+    "    sys.exit(1)\n",
+    "except ValueError as e:\n",
+    "    print(f\"Error loading data: {e}\")\n",
+    "    sys.exit(1)\n",
+    "except Exception as e:\n",
+    "    print(f\"Unexpected error: {e}\")\n",
+    "    sys.exit(1)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4a1c9720",
+   "metadata": {},
+   "source": [
+    " #### Data Structure Before Process"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "7374e276",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "--- Dataset Shape ---\n",
+      "(1000098, 52)\n",
+      "\n",
+      "--- Column Info ---\n",
+      "UnderwrittenCoverID           int64\n",
+      "PolicyID                      int64\n",
+      "TransactionMonth             object\n",
+      "IsVATRegistered                bool\n",
+      "Citizenship                  object\n",
+      "LegalType                    object\n",
+      "Title                        object\n",
+      "Language                     object\n",
+      "Bank                         object\n",
+      "AccountType                  object\n",
+      "MaritalStatus                object\n",
+      "Gender                       object\n",
+      "Country                      object\n",
+      "Province                     object\n",
+      "PostalCode                    int64\n",
+      "MainCrestaZone               object\n",
+      "SubCrestaZone                object\n",
+      "ItemType                     object\n",
+      "mmcode                      float64\n",
+      "VehicleType                  object\n",
+      "RegistrationYear              int64\n",
+      "make                         object\n",
+      "Model                        object\n",
+      "Cylinders                   float64\n",
+      "cubiccapacity               float64\n",
+      "kilowatts                   float64\n",
+      "bodytype                     object\n",
+      "NumberOfDoors               float64\n",
+      "VehicleIntroDate             object\n",
+      "CustomValueEstimate         float64\n",
+      "AlarmImmobiliser             object\n",
+      "TrackingDevice               object\n",
+      "CapitalOutstanding           object\n",
+      "NewVehicle                   object\n",
+      "WrittenOff                   object\n",
+      "Rebuilt                      object\n",
+      "Converted                    object\n",
+      "CrossBorder                  object\n",
+      "NumberOfVehiclesInFleet     float64\n",
+      "SumInsured                  float64\n",
+      "TermFrequency                object\n",
+      "CalculatedPremiumPerTerm    float64\n",
+      "ExcessSelected               object\n",
+      "CoverCategory                object\n",
+      "CoverType                    object\n",
+      "CoverGroup                   object\n",
+      "Section                      object\n",
+      "Product                      object\n",
+      "StatutoryClass               object\n",
+      "StatutoryRiskType            object\n",
+      "TotalPremium                float64\n",
+      "TotalClaims                 float64\n",
+      "dtype: object\n",
+      "\n",
+      "--- Total Missing Values (%) ---\n",
+      "NumberOfVehiclesInFleet    100.000000\n",
+      "CrossBorder                 99.930207\n",
+      "CustomValueEstimate         77.956560\n",
+      "Rebuilt                     64.183810\n",
+      "Converted                   64.183810\n",
+      "WrittenOff                  64.183810\n",
+      "NewVehicle                  15.327998\n",
+      "Bank                        14.594670\n",
+      "AccountType                  4.022806\n",
+      "Gender                       0.953507\n",
+      "MaritalStatus                0.825819\n",
+      "VehicleType                  0.055195\n",
+      "make                         0.055195\n",
+      "mmcode                       0.055195\n",
+      "Model                        0.055195\n",
+      "Cylinders                    0.055195\n",
+      "bodytype                     0.055195\n",
+      "kilowatts                    0.055195\n",
+      "NumberOfDoors                0.055195\n",
+      "VehicleIntroDate             0.055195\n",
+      "dtype: float64\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>UnderwrittenCoverID</th>\n",
+       "      <th>PolicyID</th>\n",
+       "      <th>TransactionMonth</th>\n",
+       "      <th>IsVATRegistered</th>\n",
+       "      <th>Citizenship</th>\n",
+       "      <th>LegalType</th>\n",
+       "      <th>Title</th>\n",
+       "      <th>Language</th>\n",
+       "      <th>Bank</th>\n",
+       "      <th>AccountType</th>\n",
+       "      <th>...</th>\n",
+       "      <th>ExcessSelected</th>\n",
+       "      <th>CoverCategory</th>\n",
+       "      <th>CoverType</th>\n",
+       "      <th>CoverGroup</th>\n",
+       "      <th>Section</th>\n",
+       "      <th>Product</th>\n",
+       "      <th>StatutoryClass</th>\n",
+       "      <th>StatutoryRiskType</th>\n",
+       "      <th>TotalPremium</th>\n",
+       "      <th>TotalClaims</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>145249</td>\n",
+       "      <td>12827</td>\n",
+       "      <td>2015-03-01 00:00:00</td>\n",
+       "      <td>True</td>\n",
+       "      <td></td>\n",
+       "      <td>Close Corporation</td>\n",
+       "      <td>Mr</td>\n",
+       "      <td>English</td>\n",
+       "      <td>First National Bank</td>\n",
+       "      <td>Current account</td>\n",
+       "      <td>...</td>\n",
+       "      <td>Mobility - Windscreen</td>\n",
+       "      <td>Windscreen</td>\n",
+       "      <td>Windscreen</td>\n",
+       "      <td>Comprehensive - Taxi</td>\n",
+       "      <td>Motor Comprehensive</td>\n",
+       "      <td>Mobility Metered Taxis: Monthly</td>\n",
+       "      <td>Commercial</td>\n",
+       "      <td>IFRS Constant</td>\n",
+       "      <td>21.929825</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>145249</td>\n",
+       "      <td>12827</td>\n",
+       "      <td>2015-05-01 00:00:00</td>\n",
+       "      <td>True</td>\n",
+       "      <td></td>\n",
+       "      <td>Close Corporation</td>\n",
+       "      <td>Mr</td>\n",
+       "      <td>English</td>\n",
+       "      <td>First National Bank</td>\n",
+       "      <td>Current account</td>\n",
+       "      <td>...</td>\n",
+       "      <td>Mobility - Windscreen</td>\n",
+       "      <td>Windscreen</td>\n",
+       "      <td>Windscreen</td>\n",
+       "      <td>Comprehensive - Taxi</td>\n",
+       "      <td>Motor Comprehensive</td>\n",
+       "      <td>Mobility Metered Taxis: Monthly</td>\n",
+       "      <td>Commercial</td>\n",
+       "      <td>IFRS Constant</td>\n",
+       "      <td>21.929825</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>145249</td>\n",
+       "      <td>12827</td>\n",
+       "      <td>2015-07-01 00:00:00</td>\n",
+       "      <td>True</td>\n",
+       "      <td></td>\n",
+       "      <td>Close Corporation</td>\n",
+       "      <td>Mr</td>\n",
+       "      <td>English</td>\n",
+       "      <td>First National Bank</td>\n",
+       "      <td>Current account</td>\n",
+       "      <td>...</td>\n",
+       "      <td>Mobility - Windscreen</td>\n",
+       "      <td>Windscreen</td>\n",
+       "      <td>Windscreen</td>\n",
+       "      <td>Comprehensive - Taxi</td>\n",
+       "      <td>Motor Comprehensive</td>\n",
+       "      <td>Mobility Metered Taxis: Monthly</td>\n",
+       "      <td>Commercial</td>\n",
+       "      <td>IFRS Constant</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>145255</td>\n",
+       "      <td>12827</td>\n",
+       "      <td>2015-05-01 00:00:00</td>\n",
+       "      <td>True</td>\n",
+       "      <td></td>\n",
+       "      <td>Close Corporation</td>\n",
+       "      <td>Mr</td>\n",
+       "      <td>English</td>\n",
+       "      <td>First National Bank</td>\n",
+       "      <td>Current account</td>\n",
+       "      <td>...</td>\n",
+       "      <td>Mobility - Metered Taxis - R2000</td>\n",
+       "      <td>Own damage</td>\n",
+       "      <td>Own Damage</td>\n",
+       "      <td>Comprehensive - Taxi</td>\n",
+       "      <td>Motor Comprehensive</td>\n",
+       "      <td>Mobility Metered Taxis: Monthly</td>\n",
+       "      <td>Commercial</td>\n",
+       "      <td>IFRS Constant</td>\n",
+       "      <td>512.848070</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>145255</td>\n",
+       "      <td>12827</td>\n",
+       "      <td>2015-07-01 00:00:00</td>\n",
+       "      <td>True</td>\n",
+       "      <td></td>\n",
+       "      <td>Close Corporation</td>\n",
+       "      <td>Mr</td>\n",
+       "      <td>English</td>\n",
+       "      <td>First National Bank</td>\n",
+       "      <td>Current account</td>\n",
+       "      <td>...</td>\n",
+       "      <td>Mobility - Metered Taxis - R2000</td>\n",
+       "      <td>Own damage</td>\n",
+       "      <td>Own Damage</td>\n",
+       "      <td>Comprehensive - Taxi</td>\n",
+       "      <td>Motor Comprehensive</td>\n",
+       "      <td>Mobility Metered Taxis: Monthly</td>\n",
+       "      <td>Commercial</td>\n",
+       "      <td>IFRS Constant</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5 rows × 52 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   UnderwrittenCoverID  PolicyID     TransactionMonth  IsVATRegistered  \\\n",
+       "0               145249     12827  2015-03-01 00:00:00             True   \n",
+       "1               145249     12827  2015-05-01 00:00:00             True   \n",
+       "2               145249     12827  2015-07-01 00:00:00             True   \n",
+       "3               145255     12827  2015-05-01 00:00:00             True   \n",
+       "4               145255     12827  2015-07-01 00:00:00             True   \n",
+       "\n",
+       "  Citizenship          LegalType Title Language                 Bank  \\\n",
+       "0              Close Corporation    Mr  English  First National Bank   \n",
+       "1              Close Corporation    Mr  English  First National Bank   \n",
+       "2              Close Corporation    Mr  English  First National Bank   \n",
+       "3              Close Corporation    Mr  English  First National Bank   \n",
+       "4              Close Corporation    Mr  English  First National Bank   \n",
+       "\n",
+       "       AccountType  ...                    ExcessSelected CoverCategory  \\\n",
+       "0  Current account  ...             Mobility - Windscreen    Windscreen   \n",
+       "1  Current account  ...             Mobility - Windscreen    Windscreen   \n",
+       "2  Current account  ...             Mobility - Windscreen    Windscreen   \n",
+       "3  Current account  ...  Mobility - Metered Taxis - R2000    Own damage   \n",
+       "4  Current account  ...  Mobility - Metered Taxis - R2000    Own damage   \n",
+       "\n",
+       "    CoverType            CoverGroup              Section  \\\n",
+       "0  Windscreen  Comprehensive - Taxi  Motor Comprehensive   \n",
+       "1  Windscreen  Comprehensive - Taxi  Motor Comprehensive   \n",
+       "2  Windscreen  Comprehensive - Taxi  Motor Comprehensive   \n",
+       "3  Own Damage  Comprehensive - Taxi  Motor Comprehensive   \n",
+       "4  Own Damage  Comprehensive - Taxi  Motor Comprehensive   \n",
+       "\n",
+       "                           Product StatutoryClass StatutoryRiskType  \\\n",
+       "0  Mobility Metered Taxis: Monthly     Commercial     IFRS Constant   \n",
+       "1  Mobility Metered Taxis: Monthly     Commercial     IFRS Constant   \n",
+       "2  Mobility Metered Taxis: Monthly     Commercial     IFRS Constant   \n",
+       "3  Mobility Metered Taxis: Monthly     Commercial     IFRS Constant   \n",
+       "4  Mobility Metered Taxis: Monthly     Commercial     IFRS Constant   \n",
+       "\n",
+       "   TotalPremium TotalClaims  \n",
+       "0     21.929825         0.0  \n",
+       "1     21.929825         0.0  \n",
+       "2      0.000000         0.0  \n",
+       "3    512.848070         0.0  \n",
+       "4      0.000000         0.0  \n",
+       "\n",
+       "[5 rows x 52 columns]"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "dataset_overview(df)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "530c6650",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Duplicated rows: 0\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "np.int64(0)"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "duplicated_rows(df)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "7231d6cb",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "NumberOfVehiclesInFleet    1.000000\n",
+       "CrossBorder                0.999302\n",
+       "CustomValueEstimate        0.779566\n",
+       "Rebuilt                    0.641838\n",
+       "Converted                  0.641838\n",
+       "WrittenOff                 0.641838\n",
+       "NewVehicle                 0.153280\n",
+       "Bank                       0.145947\n",
+       "AccountType                0.040228\n",
+       "Gender                     0.009535\n",
+       "MaritalStatus              0.008258\n",
+       "VehicleType                0.000552\n",
+       "make                       0.000552\n",
+       "mmcode                     0.000552\n",
+       "Model                      0.000552\n",
+       "Cylinders                  0.000552\n",
+       "bodytype                   0.000552\n",
+       "kilowatts                  0.000552\n",
+       "NumberOfDoors              0.000552\n",
+       "VehicleIntroDate           0.000552\n",
+       "dtype: float64"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "summarize_missing(df)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "99754c8b",
+   "metadata": {},
+   "source": [
+    "### Full Data Cleaning Pipeline"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "07aff5b3",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "D:\\10Acadamy\\Week 3\\Tasks\\Predictive-Modeling\\src\\insurance_analytics\\preprocessing\\cleaner.py:55: FutureWarning: errors='ignore' is deprecated and will raise in a future version. Use to_numeric without passing `errors` and catch exceptions explicitly instead\n",
+      "  df[col] = pd.to_numeric(df[col], errors=\"ignore\")\n"
+     ]
+    }
+   ],
+   "source": [
+    "# 3️⃣ Clean Data\n",
+    "\n",
+    "df_clean = cleaning(df, date_cols=[\"TransactionMonth\"])\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "71fcac4f",
+   "metadata": {},
+   "source": [
+    "# Calculate LossRatio"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "370c33fd",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df = add_loss_ratio(df_clean)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "067da5b9",
+   "metadata": {},
+   "source": [
+    " ### Data Structure After Process"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "fcfe7778",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "--- Dataset Shape ---\n",
+      "(1000098, 50)\n",
+      "\n",
+      "--- Column Info ---\n",
+      "UnderwrittenCoverID           int64\n",
+      "PolicyID                      int64\n",
+      "TransactionMonth              int64\n",
+      "IsVATRegistered                bool\n",
+      "LegalType                    object\n",
+      "Title                        object\n",
+      "Language                     object\n",
+      "Bank                         object\n",
+      "AccountType                  object\n",
+      "MaritalStatus                object\n",
+      "Gender                       object\n",
+      "Country                      object\n",
+      "Province                     object\n",
+      "PostalCode                    int64\n",
+      "MainCrestaZone               object\n",
+      "SubCrestaZone                object\n",
+      "ItemType                     object\n",
+      "mmcode                      float64\n",
+      "VehicleType                  object\n",
+      "RegistrationYear              int64\n",
+      "make                         object\n",
+      "Model                        object\n",
+      "Cylinders                   float64\n",
+      "cubiccapacity               float64\n",
+      "kilowatts                   float64\n",
+      "bodytype                     object\n",
+      "NumberOfDoors               float64\n",
+      "VehicleIntroDate             object\n",
+      "CustomValueEstimate         float64\n",
+      "AlarmImmobiliser             object\n",
+      "TrackingDevice               object\n",
+      "CapitalOutstanding           object\n",
+      "NewVehicle                   object\n",
+      "WrittenOff                   object\n",
+      "Rebuilt                      object\n",
+      "Converted                    object\n",
+      "SumInsured                  float64\n",
+      "TermFrequency                object\n",
+      "CalculatedPremiumPerTerm    float64\n",
+      "ExcessSelected               object\n",
+      "CoverCategory                object\n",
+      "CoverType                    object\n",
+      "CoverGroup                   object\n",
+      "Section                      object\n",
+      "Product                      object\n",
+      "StatutoryClass               object\n",
+      "StatutoryRiskType            object\n",
+      "TotalPremium                float64\n",
+      "TotalClaims                 float64\n",
+      "LossRatio                   float64\n",
+      "dtype: object\n",
+      "\n",
+      "--- Total Missing Values (%) ---\n",
+      "CustomValueEstimate    77.956560\n",
+      "WrittenOff             64.183810\n",
+      "Rebuilt                64.183810\n",
+      "Converted              64.183810\n",
+      "CapitalOutstanding     50.004900\n",
+      "LossRatio              38.159660\n",
+      "NewVehicle             15.327998\n",
+      "Bank                   14.594670\n",
+      "AccountType             4.022806\n",
+      "Gender                  0.953507\n",
+      "MaritalStatus           0.825819\n",
+      "VehicleType             0.055195\n",
+      "mmcode                  0.055195\n",
+      "VehicleIntroDate        0.055195\n",
+      "kilowatts               0.055195\n",
+      "cubiccapacity           0.055195\n",
+      "bodytype                0.055195\n",
+      "NumberOfDoors           0.055195\n",
+      "Model                   0.055195\n",
+      "make                    0.055195\n",
+      "dtype: float64\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>UnderwrittenCoverID</th>\n",
+       "      <th>PolicyID</th>\n",
+       "      <th>TransactionMonth</th>\n",
+       "      <th>IsVATRegistered</th>\n",
+       "      <th>LegalType</th>\n",
+       "      <th>Title</th>\n",
+       "      <th>Language</th>\n",
+       "      <th>Bank</th>\n",
+       "      <th>AccountType</th>\n",
+       "      <th>MaritalStatus</th>\n",
+       "      <th>...</th>\n",
+       "      <th>CoverCategory</th>\n",
+       "      <th>CoverType</th>\n",
+       "      <th>CoverGroup</th>\n",
+       "      <th>Section</th>\n",
+       "      <th>Product</th>\n",
+       "      <th>StatutoryClass</th>\n",
+       "      <th>StatutoryRiskType</th>\n",
+       "      <th>TotalPremium</th>\n",
+       "      <th>TotalClaims</th>\n",
+       "      <th>LossRatio</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>145249</td>\n",
+       "      <td>12827</td>\n",
+       "      <td>1425168000000000000</td>\n",
+       "      <td>True</td>\n",
+       "      <td>Close Corporation</td>\n",
+       "      <td>Mr</td>\n",
+       "      <td>English</td>\n",
+       "      <td>First National Bank</td>\n",
+       "      <td>Current account</td>\n",
+       "      <td>Not specified</td>\n",
+       "      <td>...</td>\n",
+       "      <td>Windscreen</td>\n",
+       "      <td>Windscreen</td>\n",
+       "      <td>Comprehensive - Taxi</td>\n",
+       "      <td>Motor Comprehensive</td>\n",
+       "      <td>Mobility Metered Taxis: Monthly</td>\n",
+       "      <td>Commercial</td>\n",
+       "      <td>IFRS Constant</td>\n",
+       "      <td>21.929825</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>145249</td>\n",
+       "      <td>12827</td>\n",
+       "      <td>1430438400000000000</td>\n",
+       "      <td>True</td>\n",
+       "      <td>Close Corporation</td>\n",
+       "      <td>Mr</td>\n",
+       "      <td>English</td>\n",
+       "      <td>First National Bank</td>\n",
+       "      <td>Current account</td>\n",
+       "      <td>Not specified</td>\n",
+       "      <td>...</td>\n",
+       "      <td>Windscreen</td>\n",
+       "      <td>Windscreen</td>\n",
+       "      <td>Comprehensive - Taxi</td>\n",
+       "      <td>Motor Comprehensive</td>\n",
+       "      <td>Mobility Metered Taxis: Monthly</td>\n",
+       "      <td>Commercial</td>\n",
+       "      <td>IFRS Constant</td>\n",
+       "      <td>21.929825</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>145249</td>\n",
+       "      <td>12827</td>\n",
+       "      <td>1435708800000000000</td>\n",
+       "      <td>True</td>\n",
+       "      <td>Close Corporation</td>\n",
+       "      <td>Mr</td>\n",
+       "      <td>English</td>\n",
+       "      <td>First National Bank</td>\n",
+       "      <td>Current account</td>\n",
+       "      <td>Not specified</td>\n",
+       "      <td>...</td>\n",
+       "      <td>Windscreen</td>\n",
+       "      <td>Windscreen</td>\n",
+       "      <td>Comprehensive - Taxi</td>\n",
+       "      <td>Motor Comprehensive</td>\n",
+       "      <td>Mobility Metered Taxis: Monthly</td>\n",
+       "      <td>Commercial</td>\n",
+       "      <td>IFRS Constant</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>145255</td>\n",
+       "      <td>12827</td>\n",
+       "      <td>1430438400000000000</td>\n",
+       "      <td>True</td>\n",
+       "      <td>Close Corporation</td>\n",
+       "      <td>Mr</td>\n",
+       "      <td>English</td>\n",
+       "      <td>First National Bank</td>\n",
+       "      <td>Current account</td>\n",
+       "      <td>Not specified</td>\n",
+       "      <td>...</td>\n",
+       "      <td>Own damage</td>\n",
+       "      <td>Own Damage</td>\n",
+       "      <td>Comprehensive - Taxi</td>\n",
+       "      <td>Motor Comprehensive</td>\n",
+       "      <td>Mobility Metered Taxis: Monthly</td>\n",
+       "      <td>Commercial</td>\n",
+       "      <td>IFRS Constant</td>\n",
+       "      <td>512.848070</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>145255</td>\n",
+       "      <td>12827</td>\n",
+       "      <td>1435708800000000000</td>\n",
+       "      <td>True</td>\n",
+       "      <td>Close Corporation</td>\n",
+       "      <td>Mr</td>\n",
+       "      <td>English</td>\n",
+       "      <td>First National Bank</td>\n",
+       "      <td>Current account</td>\n",
+       "      <td>Not specified</td>\n",
+       "      <td>...</td>\n",
+       "      <td>Own damage</td>\n",
+       "      <td>Own Damage</td>\n",
+       "      <td>Comprehensive - Taxi</td>\n",
+       "      <td>Motor Comprehensive</td>\n",
+       "      <td>Mobility Metered Taxis: Monthly</td>\n",
+       "      <td>Commercial</td>\n",
+       "      <td>IFRS Constant</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5 rows × 50 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   UnderwrittenCoverID  PolicyID     TransactionMonth  IsVATRegistered  \\\n",
+       "0               145249     12827  1425168000000000000             True   \n",
+       "1               145249     12827  1430438400000000000             True   \n",
+       "2               145249     12827  1435708800000000000             True   \n",
+       "3               145255     12827  1430438400000000000             True   \n",
+       "4               145255     12827  1435708800000000000             True   \n",
+       "\n",
+       "           LegalType Title Language                 Bank      AccountType  \\\n",
+       "0  Close Corporation    Mr  English  First National Bank  Current account   \n",
+       "1  Close Corporation    Mr  English  First National Bank  Current account   \n",
+       "2  Close Corporation    Mr  English  First National Bank  Current account   \n",
+       "3  Close Corporation    Mr  English  First National Bank  Current account   \n",
+       "4  Close Corporation    Mr  English  First National Bank  Current account   \n",
+       "\n",
+       "   MaritalStatus  ... CoverCategory   CoverType            CoverGroup  \\\n",
+       "0  Not specified  ...    Windscreen  Windscreen  Comprehensive - Taxi   \n",
+       "1  Not specified  ...    Windscreen  Windscreen  Comprehensive - Taxi   \n",
+       "2  Not specified  ...    Windscreen  Windscreen  Comprehensive - Taxi   \n",
+       "3  Not specified  ...    Own damage  Own Damage  Comprehensive - Taxi   \n",
+       "4  Not specified  ...    Own damage  Own Damage  Comprehensive - Taxi   \n",
+       "\n",
+       "               Section                          Product StatutoryClass  \\\n",
+       "0  Motor Comprehensive  Mobility Metered Taxis: Monthly     Commercial   \n",
+       "1  Motor Comprehensive  Mobility Metered Taxis: Monthly     Commercial   \n",
+       "2  Motor Comprehensive  Mobility Metered Taxis: Monthly     Commercial   \n",
+       "3  Motor Comprehensive  Mobility Metered Taxis: Monthly     Commercial   \n",
+       "4  Motor Comprehensive  Mobility Metered Taxis: Monthly     Commercial   \n",
+       "\n",
+       "  StatutoryRiskType  TotalPremium TotalClaims  LossRatio  \n",
+       "0     IFRS Constant     21.929825         0.0        0.0  \n",
+       "1     IFRS Constant     21.929825         0.0        0.0  \n",
+       "2     IFRS Constant      0.000000         0.0        NaN  \n",
+       "3     IFRS Constant    512.848070         0.0        0.0  \n",
+       "4     IFRS Constant      0.000000         0.0        NaN  \n",
+       "\n",
+       "[5 rows x 50 columns]"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "dataset_overview(df_clean)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "749c1617",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Duplicated rows: 0\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "np.int64(0)"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "duplicated_rows(df_clean)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "96583720",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "CustomValueEstimate    0.779566\n",
+       "WrittenOff             0.641838\n",
+       "Rebuilt                0.641838\n",
+       "Converted              0.641838\n",
+       "CapitalOutstanding     0.500049\n",
+       "LossRatio              0.381597\n",
+       "NewVehicle             0.153280\n",
+       "Bank                   0.145947\n",
+       "AccountType            0.040228\n",
+       "Gender                 0.009535\n",
+       "MaritalStatus          0.008258\n",
+       "VehicleType            0.000552\n",
+       "mmcode                 0.000552\n",
+       "VehicleIntroDate       0.000552\n",
+       "kilowatts              0.000552\n",
+       "cubiccapacity          0.000552\n",
+       "bodytype               0.000552\n",
+       "NumberOfDoors          0.000552\n",
+       "Model                  0.000552\n",
+       "make                   0.000552\n",
+       "dtype: float64"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "summarize_missing(df_clean)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e0748bdb",
+   "metadata": {},
+   "source": [
+    "### UNIVARIATE ANALYSIS"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "4341f6cc",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x500 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "D:\\10Acadamy\\Week 3\\Tasks\\Predictive-Modeling\\src\\insurance_analytics\\viz\\plots.py:60: FutureWarning: \n",
+      "\n",
+      "Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `x` variable to `hue` and set `legend=False` for the same effect.\n",
+      "\n",
+      "  ax = sns.barplot(x=counts.index, y=counts.values, palette=\"pastel\")\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1200x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "\n",
+    "# Univariate\n",
+    "plot_histogram(df_clean, \"TotalPremium\",\n",
+    "                   save_path=plots_dir / \"hist_totalpremium.png\")\n",
+    "plot_bar(df_clean, \"Province\", save_path=plots_dir / \"bar_province.png\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "55f4514f",
+   "metadata": {},
+   "source": [
+    "### Bivariate / Multivariate Analysis"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "c3902024",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1200x1000 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "D:\\10Acadamy\\Week 3\\Tasks\\Predictive-Modeling\\src\\insurance_analytics\\viz\\plots.py:173: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n",
+      "  plt.tight_layout()\n",
+      "D:\\10Acadamy\\Week 3\\Tasks\\Predictive-Modeling\\src\\insurance_analytics\\viz\\plots.py:176: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n",
+      "  plt.savefig(save_path, dpi=300)\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 1200x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "\n",
+    "# Bivariate / Multivariate\n",
+    "correlation_matrix(df_clean, numeric_cols=[\n",
+    "                       \"TotalPremium\", \"TotalClaims\", \"LossRatio\"], save_path=plots_dir / \"corr_matrix.png\")\n",
+    "scatter_by_group(df_clean, \"TotalPremium\", \"TotalClaims\", \"VehicleType\",\n",
+    "                     save_path=plots_dir / \"scatter_premium_claims.png\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e6e6caa3",
+   "metadata": {},
+   "source": [
+    "### Outlier Detection"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "8252d8a0",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "TotalClaims: 2793 outliers detected\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Outliers\n",
+    "outliers = detect_outliers_iqr(df_clean, \"TotalClaims\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "3913b359",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Boxplot\n",
+    "boxplot_outliers(df_clean, \"TotalClaims\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "b612a002",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "d:\\10Acadamy\\Week 3\\Tasks\\Predictive-Modeling\\.venv\\Lib\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n",
+      "  fig.canvas.print_figure(bytes_io, **kw)\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 1200x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Scatter plot relative to Premium\n",
+    "scatter_outliers(df_clean, x_col=\"TotalPremium\",\n",
+    "                 y_col=\"TotalClaims\", hue_col=\"VehicleType\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "4dce20fe",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1200x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Highlight IQR-based outliers\n",
+    "highlight_outliers_iqr(df_clean, \"TotalClaims\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "1a18ab20",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[IO] CSV file is saved sucessfully\n",
+      "[IO] CSV saved successfully\n"
+     ]
+    }
+   ],
+   "source": [
+    "path =  processed_dir /  \"insurance_cleaned.csv\"\n",
+    "         \n",
+    "write_csv(df=df_clean,path=path)\n",
+    "print(f\"[IO] CSV saved successfully\")"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": ".venv",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.13.1"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/tests/features.py b/scripts/__init__.py
similarity index 100%
rename from tests/features.py
rename to scripts/__init__.py
diff --git a/scripts/__pycache__/run_eda.cpython-311.pyc b/scripts/__pycache__/run_eda.cpython-311.pyc
deleted file mode 100644
index 1815e0e..0000000
Binary files a/scripts/__pycache__/run_eda.cpython-311.pyc and /dev/null differ
diff --git a/scripts/clean_data.py b/scripts/clean_data.py
new file mode 100644
index 0000000..e69de29
diff --git a/scripts/run_eda.py b/scripts/run_eda.py
deleted file mode 100644
index 2c9a9c7..0000000
--- a/scripts/run_eda.py
+++ /dev/null
@@ -1,60 +0,0 @@
-# scripts/run_eda.py
-import os
-from src.utils.data_loader import load_csv, save_csv
-from src.eda.eda_tools import (
-    data_structure, descriptive_stats, overall_loss_ratio, loss_ratio_by_group,
-    plot_loss_ratio_by_province, plot_totalclaims_distribution,
-    plot_claims_premium_time_series, scatter_premium_vs_claims, outlier_summary
-)
-import argparse
-import json
-
-def main(input_path, output_dir):
-    os.makedirs(output_dir, exist_ok=True)
-    figures_dir = os.path.join(output_dir, "figures")
-    os.makedirs(figures_dir, exist_ok=True)
-    summaries_dir = os.path.join(output_dir, "summaries")
-    os.makedirs(summaries_dir, exist_ok=True)
-
-    print("Loading data:", input_path)
-    df = load_csv(input_path, parse_dates=["TransactionMonth", "VehicleIntroDate"])
-
-    # Basic data structure
-    structure = data_structure(df)
-    structure.to_csv(os.path.join(summaries_dir, "data_structure.csv"))
-
-    # Descriptive stats for key numeric columns
-    numeric_cols = ["TotalPremium", "TotalClaims", "CustomValueEstimate"]
-    present = [c for c in numeric_cols if c in df.columns]
-    stats = descriptive_stats(df, present)
-    stats.to_csv(os.path.join(summaries_dir, "descriptive_stats.csv"))
-
-    # Compute Loss Ratios
-    overall_lr = overall_loss_ratio(df)
-    lr_by_province = loss_ratio_by_group(df, "Province").reset_index()
-    lr_by_province.to_csv(os.path.join(summaries_dir, "loss_ratio_by_province.csv"))
-    with open(os.path.join(summaries_dir, "loss_ratio_overall.json"), "w") as f:
-        json.dump({"overall_loss_ratio": overall_lr}, f, default=str)
-
-    # Outliers summary
-    outlier_tc = outlier_summary(df, "TotalClaims") if "TotalClaims" in df.columns else {}
-    with open(os.path.join(summaries_dir, "outlier_totalclaims.json"), "w") as f:
-        json.dump(outlier_tc, f, default=str)
-
-    # Create required 3 beautiful plots
-    p1 = plot_loss_ratio_by_province(df, figures_dir)
-    p2 = plot_totalclaims_distribution(df, figures_dir)
-    p3 = plot_claims_premium_time_series(df, figures_dir)
-    p4 = scatter_premium_vs_claims(df, figures_dir)
-
-    print("Saved figures:", p1, p2, p3, p4)
-    print("Summaries saved to", summaries_dir)
-    print("Overall Loss Ratio:", overall_lr)
-    print("EDA complete")
-
-if __name__ == "__main__":
-    parser = argparse.ArgumentParser(description="Run EDA for ACIS dataset")
-    parser.add_argument("--input", default="data/raw/data.csv")
-    parser.add_argument("--output", default="reports")
-    args = parser.parse_args()
-    main(args.input, args.output)
diff --git a/scripts/run_eda_pipeline.py b/scripts/run_eda_pipeline.py
new file mode 100644
index 0000000..3031c1a
--- /dev/null
+++ b/scripts/run_eda_pipeline.py
@@ -0,0 +1,111 @@
+# run_eda_pipeline.py
+
+from pathlib import Path
+import pandas as pd
+
+from insurance_analytics.core.registry import settings
+from insurance_analytics.preprocessing.cleaner import cleaning
+from insurance_analytics.data.load_data import load_data
+from insurance_analytics.preprocessing.feature_engineering import add_loss_ratio
+from insurance_analytics.eda.exploration import (
+    dataset_overview, duplicated_rows, detect_outliers_iqr
+)
+from insurance_analytics.viz.plots import (
+    plot_histogram,
+    plot_bar,
+    correlation_matrix,
+    boxplot_outliers,
+    highlight_outliers_iqr,
+    scatter_by_group
+)
+
+
+def run_eda_pipeline(file_name: str):
+    """
+    Full EDA pipeline: load, clean, feature engineer, explore, visualize.
+    """
+    # -----------------------------
+    # Paths
+    # -----------------------------
+    raw_path = settings.DATA["raw_dir"] / file_name
+    report_path = Path(settings.REPORTS["reports_dir"])
+    plots_dir = Path(settings.REPORTS["plots_dir"])
+    processed_dir = Path(settings.DATA["processed_dir"])
+
+    # Ensure directories exist
+    report_path.mkdir(parents=True, exist_ok=True)
+    plots_dir.mkdir(parents=True, exist_ok=True)
+    processed_dir.mkdir(parents=True, exist_ok=True)
+
+    # -----------------------------
+    # Load data
+    # -----------------------------
+    print(f"Loading data from {raw_path}...")
+    df = load_data(raw_path)
+
+    # -----------------------------
+    # Cleaning
+    # -----------------------------
+    print("Cleaning data...")
+    df = cleaning(df, report_missing=True)
+
+    # -----------------------------
+    # Feature Engineering
+    # -----------------------------
+    print("Adding LossRatio feature...")
+    df = add_loss_ratio(df)
+
+    # -----------------------------
+    # Dataset Overview
+    # -----------------------------
+    print("\nDataset Overview:")
+    dataset_overview(df)
+
+    # Check for duplicates
+    print("\nChecking for duplicates...")
+    dup_count = duplicated_rows(df)
+
+    # -----------------------------
+    # Univariate Analysis
+    # -----------------------------
+    numeric_cols = df.select_dtypes(include="number").columns.tolist()
+    cat_cols = df.select_dtypes(include="object").columns.tolist()
+
+    # Plot numeric distributions
+    for col in numeric_cols[:5]:  # limit first 5 for speed
+        plot_histogram(df, col, save_path=plots_dir / f"{col}_hist.png")
+        boxplot_outliers(df, col, save_path=plots_dir / f"{col}_box.png")
+
+    # Plot categorical distributions
+    for col in cat_cols[:5]:
+        plot_bar(df, col, top_n=15, save_path=plots_dir / f"{col}_bar.png")
+
+    # -----------------------------
+    # Outlier Detection
+    # -----------------------------
+    for col in numeric_cols[:5]:
+        detect_outliers_iqr(df, col)
+        highlight_outliers_iqr(
+            df, col, save_path=plots_dir / f"{col}_outliers.png")
+
+    # -----------------------------
+    # Bivariate / Multivariate Analysis
+    # -----------------------------
+    correlation_matrix(df, save_path=plots_dir / "correlation_matrix.png")
+
+    if "TotalPremium" in df.columns and "TotalClaims" in df.columns:
+        scatter_by_group(df, x_col="TotalPremium", y_col="TotalClaims",
+                         hue_col="VehicleType",
+                         save_path=plots_dir / "claims_vs_premium_vehicle.png")
+
+    # -----------------------------
+    # Save cleaned & processed dataset
+    # -----------------------------
+    processed_file = processed_dir / \
+        f"{file_name.replace('.txt', '_cleaned.csv')}"
+    df.to_csv(processed_file, index=False)
+    print(f"\nProcessed dataset saved to {processed_file}")
+
+
+if __name__ == "__main__":
+    run_eda_pipeline("MachineLearningRating_v3.txt")
diff --git a/scripts/run_hypothesis_tests.py b/scripts/run_hypothesis_tests.py
new file mode 100644
index 0000000..e69de29
diff --git a/scripts/train_models.py b/scripts/train_models.py
new file mode 100644
index 0000000..e69de29
diff --git a/src/eda/__pycache__/eda_tools.cpython-311.pyc b/src/eda/__pycache__/eda_tools.cpython-311.pyc
deleted file mode 100644
index ec777fc..0000000
Binary files a/src/eda/__pycache__/eda_tools.cpython-311.pyc and /dev/null differ
diff --git a/src/eda/eda_tools.py b/src/eda/eda_tools.py
deleted file mode 100644
index 5af9bfe..0000000
--- a/src/eda/eda_tools.py
+++ /dev/null
@@ -1,120 +0,0 @@
-# src/eda/eda_tools.py
-import os
-import pandas as pd
-import numpy as np
-import matplotlib.pyplot as plt
-import seaborn as sns
-from typing import Tuple
-
-sns.set(style="whitegrid", rc={"figure.dpi": 150})
-
-def ensure_dir(path: str):
-    os.makedirs(path, exist_ok=True)
-
-# ---------- Summaries ----------
-def data_structure(df: pd.DataFrame) -> pd.DataFrame:
-    """Return dtypes and non-null counts."""
-    info = pd.DataFrame({
-        "dtype": df.dtypes.astype(str),
-        "non_null_count": df.count(),
-        "null_count": df.isna().sum(),
-        "unique": df.nunique(dropna=False)
-    })
-    return info
-
-def descriptive_stats(df: pd.DataFrame, cols: list) -> pd.DataFrame:
-    return df[cols].describe().T
-
-# ---------- Business Metric: Loss Ratio ----------
-def overall_loss_ratio(df: pd.DataFrame) -> float:
-    total_claims = df["TotalClaims"].sum(skipna=True)
-    total_premium = df["TotalPremium"].sum(skipna=True)
-    if total_premium == 0:
-        return np.nan
-    return total_claims / total_premium
-
-def loss_ratio_by_group(df: pd.DataFrame, group_col: str) -> pd.DataFrame:
-    grp = df.groupby(group_col)[["TotalPremium","TotalClaims"]].sum()
-    grp = grp.assign(LossRatio = grp["TotalClaims"] / grp["TotalPremium"])
-    grp = grp.sort_values("LossRatio", ascending=False)
-    return grp
-
-# ---------- Time series ----------
-def monthly_claims_premiums(df: pd.DataFrame, date_col: str = "TransactionMonth") -> pd.DataFrame:
-    df = df.copy()
-    df[date_col] = pd.to_datetime(df[date_col], errors="coerce")
-    df = df.dropna(subset=[date_col])
-    monthly = df.groupby(pd.Grouper(key=date_col, freq="MS"))[["TotalClaims","TotalPremium"]].sum()
-    monthly["ClaimFrequency"] = df.groupby(pd.Grouper(key=date_col, freq="MS"))["TotalClaims"].apply(lambda s: (s>0).sum())
-    # severity: average claim amount per claim (avoid div by zero)
-    monthly["ClaimSeverity"] = monthly.apply(lambda r: r["TotalClaims"] / max(r["ClaimFrequency"], 1), axis=1)
-    return monthly
-
-# ---------- Outlier detection ----------
-def outlier_summary(df: pd.DataFrame, col: str) -> dict:
-    s = df[col].dropna()
-    q1, q3 = s.quantile([0.25, 0.75])
-    iqr = q3 - q1
-    lower = q1 - 1.5 * iqr
-    upper = q3 + 1.5 * iqr
-    return {"q1": q1, "q3": q3, "iqr": iqr, "lower": lower, "upper": upper,
-            "n_outliers": ((s < lower) | (s > upper)).sum()}
-
-# ---------- Plots (3 required polished plots) ----------
-def plot_loss_ratio_by_province(df: pd.DataFrame, outdir: str):
-    ensure_dir(outdir)
-    grp = loss_ratio_by_group(df, "Province")
-    plt.figure(figsize=(10,6))
-    sns.barplot(x=grp.index, y=grp["LossRatio"])
-    plt.xticks(rotation=45, ha="right")
-    plt.ylabel("Loss Ratio (TotalClaims / TotalPremium)")
-    plt.title("Loss Ratio by Province")
-    plt.tight_layout()
-    path = os.path.join(outdir, "loss_ratio_by_province.png")
-    plt.savefig(path)
-    plt.close()
-    return path
-
-def plot_totalclaims_distribution(df: pd.DataFrame, outdir: str):
-    ensure_dir(outdir)
-    plt.figure(figsize=(8,5))
-    # log scale helps when heavy skew/outliers
-    sns.histplot(df["TotalClaims"].dropna(), bins=100, kde=True)
-    plt.xscale('symlog')  # symmetric log to keep zeros visible
-    plt.xlabel("TotalClaims (symlog scale)")
-    plt.title("Distribution of TotalClaims (log-friendly)")
-    plt.tight_layout()
-    path = os.path.join(outdir, "totalclaims_distribution.png")
-    plt.savefig(path)
-    plt.close()
-    return path
-
-def plot_claims_premium_time_series(df: pd.DataFrame, outdir: str, date_col="TransactionMonth"):
-    ensure_dir(outdir)
-    monthly = monthly_claims_premiums(df, date_col=date_col)
-    plt.figure(figsize=(10,6))
-    ax = monthly[["TotalClaims","TotalPremium"]].plot(title="Monthly TotalClaims vs TotalPremium")
-    ax.set_ylabel("Amount (local currency)")
-    plt.tight_layout()
-    path = os.path.join(outdir, "monthly_claims_premium.png")
-    plt.savefig(path)
-    plt.close()
-    return path
-
-# ---------- Bivariate exploration ----------
-def scatter_premium_vs_claims(df: pd.DataFrame, outdir: str, sample=10000):
-    ensure_dir(outdir)
-    n = min(len(df), sample)
-    sample_df = df.sample(n=n, random_state=42)
-    plt.figure(figsize=(8,6))
-    sns.scatterplot(x=sample_df["TotalPremium"], y=sample_df["TotalClaims"], alpha=0.6)
-    plt.xscale("symlog")
-    plt.yscale("symlog")
-    plt.xlabel("TotalPremium (symlog)")
-    plt.ylabel("TotalClaims (symlog)")
-    plt.title(f"Scatter: TotalPremium vs TotalClaims (sample n={n})")
-    plt.tight_layout()
-    path = os.path.join(outdir, "scatter_premium_vs_claims.png")
-    plt.savefig(path)
-    plt.close()
-    return path
diff --git a/src/features/build_features.py b/src/features/build_features.py
deleted file mode 100644
index 1ff6c15..0000000
--- a/src/features/build_features.py
+++ /dev/null
@@ -1,4 +0,0 @@
-def build_features(df):
-    df["VehicleAge"] = 2025 - df["RegistrationYear"]
-    df["ClaimOccurred"] = (df["TotalClaims"] > 0).astype(int)
-    return df
diff --git a/src/insurance_analytics.egg-info/PKG-INFO b/src/insurance_analytics.egg-info/PKG-INFO
new file mode 100644
index 0000000..aa71cb9
--- /dev/null
+++ b/src/insurance_analytics.egg-info/PKG-INFO
@@ -0,0 +1,285 @@
+Metadata-Version: 2.4
+Name: insurance_analytics
+Version: 0.1.0
+Summary: Insurance Risk Analytics: End-to-end data analysis, modeling, and reporting.
+Author-email: "Tibebu K." <wise.tibec@gmail.com>
+License: MIT
+Project-URL: Homepage, https://github.com/tib-dev/preditctive-modeling
+Project-URL: Documentation, https://github.com/tib-dev/preditctive-modeling/docs
+Project-URL: Source, https://github.com/tib-dev/preditctive-modeling
+Keywords: insurance,analytics,machine-learning,risk,predictive-modeling
+Classifier: Programming Language :: Python :: 3.13
+Classifier: Programming Language :: Python :: 3 :: Only
+Classifier: License :: OSI Approved :: MIT License
+Classifier: Operating System :: OS Independent
+Requires-Python: <4.0,>=3.10
+Description-Content-Type: text/markdown
+Requires-Dist: numpy
+Requires-Dist: pandas
+Requires-Dist: pyyaml
+Requires-Dist: python-dateutil
+Requires-Dist: scikit-learn
+Requires-Dist: matplotlib
+Requires-Dist: seaborn
+Requires-Dist: plotly
+Requires-Dist: reportlab
+Requires-Dist: python-dotenv
+Provides-Extra: dev
+Requires-Dist: pytest; extra == "dev"
+Requires-Dist: pytest-cov; extra == "dev"
+Requires-Dist: black; extra == "dev"
+Requires-Dist: isort; extra == "dev"
+Requires-Dist: flake8; extra == "dev"
+Requires-Dist: pre-commit; extra == "dev"
+Requires-Dist: mypy; extra == "dev"
+Requires-Dist: codecov; extra == "dev"
+Provides-Extra: fast
+Requires-Dist: rapidfuzz; extra == "fast"
+
+# Insurance Risk Analytics & Predictive Modeling — ACIS Project
+
+Analyze historical car insurance data from AlphaCare Insurance Solutions (ACIS) in South Africa to uncover low-risk segments, optimize premiums, and support data-driven marketing strategies.
+
+---
+
+## Table of Contents
+
+- [Project Overview](#project-overview)
+- [Business Objective](#business-objective)
+- [Dataset Overview](#dataset-overview)
+- [Folder Structure](#folder-structure)
+- [Architecture](#architecture)
+- [Setup & Installation](#setup--installation)
+- [Tasks Completed](#tasks-completed)
+- [Technologies Used](#technologies-used)
+- [Key Insights](#key-insights)
+
+---
+
+## Project Overview
+
+This project covers end-to-end insurance risk analytics and predictive modeling:
+
+- Understanding historical insurance policies, claims, and premiums
+- Exploratory Data Analysis (EDA) to identify patterns and outliers
+- Statistical hypothesis testing to validate risk drivers
+- Feature engineering and data preprocessing
+- Predictive modeling for:
+  - Claim severity
+  - Premium optimization
+  - Probability of claim occurrence
+- Visualization and reporting of insights for business strategy
+
+---
+
+## Business Objective
+
+AlphaCare Insurance Solutions aims to:
+
+- Identify low-risk segments for targeted marketing
+- Optimize premiums to attract new clients while maintaining profitability
+- Understand key factors driving claims and losses
+- Support strategic decisions for product design and pricing
+
+---
+
+## Dataset Overview
+
+**Historical Insurance Data (Feb 2014 – Aug 2015):**
+
+| Column                | Description                                      |
+| --------------------- | ------------------------------------------------ |
+| PolicyID              | Unique insurance policy ID                       |
+| TransactionMonth      | Month of policy transaction                      |
+| Gender, MaritalStatus | Client demographics                              |
+| Province, PostalCode   | Client location                                  |
+| VehicleType, Make, Model | Vehicle details                                |
+| TotalPremium           | Premium paid                                     |
+| TotalClaims            | Claim amount                                     |
+| CalculatedPremiumPerTerm | Premium per term                               |
+| SumInsured             | Coverage amount                                  |
+| CoverType, Product     | Insurance plan details                            |
+
+- Includes policy, client, location, vehicle, plan, payment, and claim data
+- Data stored as CSV files for reproducibility
+
+---
+
+## Folder Structure
+
+```text
+ACIS-Insurance-Risk-Analytics/
+├── .github/                         # GitHub automation & CI/CD
+│   └── workflows/
+│       ├── ci.yml                   # Continuous Integration (unit tests, lint, smoke tests)
+│       └── codeql.yml               # Security/static analysis via CodeQL
+├── configs/                         # Configuration files for reproducible runs
+│   ├── data.yaml                    # Paths to raw/interim/processed datasets
+│   ├── modeling.yaml                # Model hyperparameters and training settings
+│   └── dvc_remote.yaml              # DVC remote storage configuration
+├── data/                             # All datasets; tracked with DVC for reproducibility
+│   ├── raw/                         # Original unprocessed CSVs or source files
+│   ├── interim/                     # Cleaned/partially processed files ready for analysis
+│   ├── processed/                   # Final datasets used for modeling
+│   └── postgres_exports/            # Exports or snapshots from PostgreSQL if needed
+├── docs/                             # Documentation for project methodology and outputs
+│   ├── README_project_overview.md   # High-level project description and objectives
+│   ├── eda_methodology.md           # Exploratory Data Analysis approaches
+│   ├── hypothesis_testing.md        # Statistical testing methodology
+│   ├── modeling_pipeline.md         # ML modeling steps and rationale
+│   ├── feature_engineering.md       # Features derived, transformations applied
+│   └── insights_report_template.md  # Template for final business insights report
+├── notebooks/                        # Interactive Jupyter notebooks
+│   ├── exploration/                  # EDA notebooks
+│   │   ├── data_overview.ipynb      # Data loading, initial summary statistics
+│   │   └── eda_visualizations.ipynb # Plots, distribution checks, temporal trends
+│   └── analysis/                     # Hypothesis testing & modeling
+│       ├── hypothesis_tests.ipynb   # A/B tests, chi-square, ANOVA, t-tests
+│       └── model_building.ipynb     # Feature prep, model training, evaluation
+├── reports/                          # Final deliverables
+│   ├── insight_report.md/           # Detailed analytics report with findings
+│   └── executive_summary.md/        # High-level recommendations for business
+├── scripts/                          # Standalone Python scripts for automation
+│   ├── __init__.py
+│   ├── load_data.py                 # Load raw data into Python objects
+│   ├── clean_data.py                # Cleaning functions for missing/erroneous values
+│   ├── feature_engineering.py       # Derive new features for modeling
+│   ├── run_eda.py                   # Script to execute all EDA tasks
+│   ├── run_hypothesis_tests.py      # Run all statistical hypothesis tests
+│   ├── train_models.py              # Train machine learning models
+│   ├── predict_premiums.py          # Predict optimized premiums using trained models
+│   └── generate_report.py           # Generate final report in markdown or PDF
+├── src/acis_insurance_analytics/     # Core modular Python package
+│   ├── __init__.py
+│   ├── config.py                    # Centralized config management (paths, parameters)
+│   ├── data/                         # Data handling functions
+│   │   ├── __init__.py
+│   │   ├── loaders.py               # Load datasets from CSV/DVC/local DB
+│   │   └── cleaners.py              # Cleaning routines, missing value handling
+│   ├── features/                     # Feature engineering modules
+│   │   ├── __init__.py
+│   │   └── build_features.py        # Generate derived features (vehicle_age, risk_flags)
+│   ├── models/                       # ML model building & inference
+│   │   ├── __init__.py
+│   │   ├── train.py                 # Train ML models (regression/classification)
+│   │   └── predict.py               # Use models to predict claims/premiums
+│   ├── hypothesis/                   # Statistical testing modules
+│   │   ├── __init__.py
+│   │   ├── tests.py                 # Functions to perform A/B tests, chi-square, t-tests
+│   │   └── metrics.py               # KPI calculations: claim frequency, severity, margin
+│   ├── viz/                          # Visualization utilities
+│   │   ├── __init__.py
+│   │   ├── plots.py                 # EDA and model performance plots
+│   │   └── dashboard.py             # Optional: assemble multiple plots into summary dashboards
+│   └── utils/                        # Utility functions
+│       ├── __init__.py
+│       ├── helpers.py               # General helper functions
+│       ├── io_utils.py              # File I/O helpers
+│       └── stats_utils.py           # Statistical functions used across tests/models
+├── tests/                           # Unit & integration tests
+│   ├── unit/
+│   │   ├── test_loaders.py
+│   │   ├── test_cleaners.py
+│   │   ├── test_features.py
+│   │   ├── test_hypothesis.py
+│   │   └── test_models.py
+│   └── integration/
+│       ├── test_full_pipeline.py     # Test full flow from data to predictions
+│       └── test_dvc_integration.py   # Ensure DVC-tracked data reproducibility
+├── requirements.txt                  # Python dependencies for production
+├── requirements-dev.txt              # Additional dev/test dependencies
+├── pyproject.toml                    # Project metadata for packaging
+├── README.md                         # Project overview, instructions
+├── .env                              # Environment variables (DB creds, API keys)
+└── .gitignore                        # Ignore unnecessary files for git
+
+```
+
+## Architecture
+```text
++------------------------------+
+|  Raw Insurance Data          |
+|  (Policies, Clients, Cars,   |
+|   Claims, Premiums)          |
+|  Location: data/raw/         |
++--------------+---------------+
+               |
+               v
++------------------------------+
+|  Data Loading & Initial Prep |
+|  src/acis_insurance_analytics/data/loaders.py
+|  Scripts: scripts/load_data.py
+|  - Load raw CSVs or database exports
+|  - Validate schema & datatypes
+|  - Initial missing value checks
++--------------+---------------+
+               |
+               v
++------------------------------+
+|  Data Cleaning & Feature Engineering |
+|  src/acis_insurance_analytics/data/cleaners.py
+|  src/acis_insurance_analytics/features/build_features.py
+|  Scripts: scripts/clean_data.py, scripts/feature_engineering.py
+|  - Handle missing/erroneous values
+|  - Derive features: vehicle age, claim flags, risk indicators
+|  - Encode categorical variables
+|  - Normalize/scale numerical features
++--------------+---------------+
+               |
+               v
++------------------------------+
+|  Processed Datasets          |
+|  data/processed/*.csv         |
+|  - Cleaned and feature-rich datasets
+|  - Ready for analysis and modeling
++--------------+---------------+
+               |
+               v
++------------------------------+
+|  Exploratory Data Analysis (EDA) & Visualization |
+|  src/acis_insurance_analytics/eda/*
+|  src/acis_insurance_analytics/viz/plots.py
+|  Scripts: scripts/run_eda.py
+|  - Descriptive stats (Loss Ratios, Claim Frequency/Severity)
+|  - Univariate, bivariate, temporal analyses
+|  - Outlier detection & data distribution checks
+|  - Visualizations: histograms, boxplots, correlation heatmaps, trends
++--------------+---------------+
+               |
+               v
++------------------------------+
+|  Statistical Testing / Hypotheses |
+|  src/acis_insurance_analytics/hypothesis/tests.py
+|  src/acis_insurance_analytics/hypothesis/metrics.py
+|  Scripts: scripts/run_hypothesis_tests.py
+|  - A/B testing: risk by province, zip code, gender
+|  - KPIs: Claim Frequency, Claim Severity, Margin
+|  - Statistical tests: t-test, chi-square, ANOVA
+|  - Results feed segmentation strategy & business recommendations
++--------------+---------------+
+               |
+               v
++------------------------------+
+|  Predictive Modeling         |
+|  src/acis_insurance_analytics/models/train.py
+|  src/acis_insurance_analytics/models/predict.py
+|  Scripts: scripts/train_models.py, scripts/predict_premiums.py
+|  - Claim Severity Regression (TotalClaims > 0)
+|  - Claim Probability Classification (binary outcome)
+|  - Premium Optimization: Risk-based pricing
+|  - Model evaluation: RMSE, R², Accuracy, Precision, Recall, F1
+|  - Interpretability: SHAP / LIME feature importance
++--------------+---------------+
+               |
+               v
++------------------------------+
+|  Reports & Business Insights |
+|  reports/insight_report.md
+|  reports/executive_summary.md
+|  Scripts: scripts/generate_report.py
+|  - Summarize EDA, hypothesis testing, model results
+|  - Highlight key risk factors & low-risk segments
+|  - Actionable recommendations: premium adjustments, marketing focus
++------------------------------+
+
+```
diff --git a/src/insurance_analytics.egg-info/SOURCES.txt b/src/insurance_analytics.egg-info/SOURCES.txt
new file mode 100644
index 0000000..ff8eef2
--- /dev/null
+++ b/src/insurance_analytics.egg-info/SOURCES.txt
@@ -0,0 +1,36 @@
+README.md
+pyproject.toml
+src/insurance_analytics/__init__.py
+src/insurance_analytics.egg-info/PKG-INFO
+src/insurance_analytics.egg-info/SOURCES.txt
+src/insurance_analytics.egg-info/dependency_links.txt
+src/insurance_analytics.egg-info/requires.txt
+src/insurance_analytics.egg-info/top_level.txt
+src/insurance_analytics/core/__init__.py
+src/insurance_analytics/core/config.py
+src/insurance_analytics/core/logger.py
+src/insurance_analytics/core/registry.py
+src/insurance_analytics/core/scheduler.py
+src/insurance_analytics/data/__init__.py
+src/insurance_analytics/data/load_data.py
+src/insurance_analytics/data/versioning.py
+src/insurance_analytics/eda/__init__.py
+src/insurance_analytics/eda/exploration.py
+src/insurance_analytics/eda/visualization.py
+src/insurance_analytics/models/__init__.py
+src/insurance_analytics/models/evaluation.py
+src/insurance_analytics/models/interpretability.py
+src/insurance_analytics/models/linear_regression.py
+src/insurance_analytics/models/random_forest.py
+src/insurance_analytics/models/xgboost_model.py
+src/insurance_analytics/preprocessing/__init__.py
+src/insurance_analytics/preprocessing/cleaner.py
+src/insurance_analytics/preprocessing/feature_engineering.py
+src/insurance_analytics/utils/__init__.py
+src/insurance_analytics/utils/io_utils.py
+src/insurance_analytics/utils/metrics.py
+src/insurance_analytics/utils/project_root.py
+src/insurance_analytics/utils/system.py
+src/insurance_analytics/utils/validation.py
+src/insurance_analytics/viz/__init__.py
+src/insurance_analytics/viz/plots.py
\ No newline at end of file
diff --git a/src/insurance_analytics.egg-info/dependency_links.txt b/src/insurance_analytics.egg-info/dependency_links.txt
new file mode 100644
index 0000000..8b13789
--- /dev/null
+++ b/src/insurance_analytics.egg-info/dependency_links.txt
@@ -0,0 +1 @@
+
diff --git a/src/insurance_analytics.egg-info/requires.txt b/src/insurance_analytics.egg-info/requires.txt
new file mode 100644
index 0000000..b5736d3
--- /dev/null
+++ b/src/insurance_analytics.egg-info/requires.txt
@@ -0,0 +1,23 @@
+numpy
+pandas
+pyyaml
+python-dateutil
+scikit-learn
+matplotlib
+seaborn
+plotly
+reportlab
+python-dotenv
+
+[dev]
+pytest
+pytest-cov
+black
+isort
+flake8
+pre-commit
+mypy
+codecov
+
+[fast]
+rapidfuzz
diff --git a/src/insurance_analytics.egg-info/top_level.txt b/src/insurance_analytics.egg-info/top_level.txt
new file mode 100644
index 0000000..a7a06cd
--- /dev/null
+++ b/src/insurance_analytics.egg-info/top_level.txt
@@ -0,0 +1 @@
+insurance_analytics
diff --git a/src/insurance_analytics/__init__.py b/src/insurance_analytics/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/src/insurance_analytics/core/__init__.py b/src/insurance_analytics/core/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/src/insurance_analytics/core/config.py b/src/insurance_analytics/core/config.py
new file mode 100644
index 0000000..33523ac
--- /dev/null
+++ b/src/insurance_analytics/core/config.py
@@ -0,0 +1,38 @@
+from pathlib import Path
+import yaml
+import logging
+
+logger = logging.getLogger(__name__)
+
+
+def load_config(path: str | None = None) -> dict:
+    """
+    Load a YAML configuration file safely.
+    If path is None, load from top-level configs folder.
+    """
+    if path is None:
+        from insurance_analytics.utils.project_root import get_project_root
+        path = get_project_root() / "configs" / "data.yaml"
+    path = Path(path)
+
+    try:
+        if not path.exists():
+            logger.error(f"Config file not found: {path.resolve()}")
+            return {}
+
+        with path.open("r", encoding="utf-8") as f:
+            data = yaml.safe_load(f)
+
+        if not isinstance(data, dict):
+            logger.error(f"Config root must be a dictionary: {path.resolve()}")
+            return {}
+
+        return data
+
+    except yaml.YAMLError as e:
+        logger.error(f"YAML parsing error in {path.resolve()}: {e}")
+        return {}
+
+    except Exception as e:
+        logger.error(f"Unexpected error loading config {path.resolve()}: {e}")
+        return {}
diff --git a/src/insurance_analytics/core/logger.py b/src/insurance_analytics/core/logger.py
new file mode 100644
index 0000000..ac9ac9a
--- /dev/null
+++ b/src/insurance_analytics/core/logger.py
@@ -0,0 +1,37 @@
+"""
+Application logger setup.
+"""
+
+import logging
+import os
+from core.config import LOG_DIR
+
+
+def get_logger(name="app"):
+    """
+    Create or retrieve a logging instance.
+
+    Args:
+        name (str): Logger name.
+
+    Returns:
+        logging.Logger | None: Logger instance or None on failure.
+    """
+    try:
+        logger = logging.getLogger(name)
+        logger.setLevel(logging.INFO)
+
+        log_path = os.path.join(LOG_DIR, "app.log")
+        handler = logging.FileHandler(log_path)
+
+        formatter = logging.Formatter(
+            "%(asctime)s - %(levelname)s - %(message)s")
+        handler.setFormatter(formatter)
+
+        if not logger.handlers:
+            logger.addHandler(handler)
+
+        return logger
+    except Exception as e:
+        print(f"[LOGGER] Logger setup failed: {e}")
+        return None
diff --git a/src/insurance_analytics/core/registry.py b/src/insurance_analytics/core/registry.py
new file mode 100644
index 0000000..d91b99b
--- /dev/null
+++ b/src/insurance_analytics/core/registry.py
@@ -0,0 +1,116 @@
+from pathlib import Path
+from typing import Dict, Optional
+from insurance_analytics.utils.project_root import get_project_root
+from insurance_analytics.utils.validation import validate_config_structure
+from insurance_analytics.core.config import load_config
+
+# Default structure if keys are missing in config
+DEFAULT_STRUCTURE = {
+    "data": {
+        "data_dir": "data",
+        "raw_dir": "data/raw",
+        "processed_dir": "data/processed",
+    },
+    "reports": {
+        "reports_dir": "reports",
+        "plots": "reports/plots",
+    },
+    
+    "logs": {"logs_dir": "logs"},
+    "models": {
+        "model_dir": "src/insurance_analytics/models",
+        "checkpoints": "src/insurance_analytics/models/checkpoints"
+    },
+    "artifacts": {"artifacts_dir": "artifacts"},
+    "docs": {"docs_dir": "docs"},
+    "notebooks": {"notebooks_dir": "notebooks"},
+    "scripts": {"scripts_dir": "scripts"},
+    "tests": {"tests_dir": "tests"},
+}
+
+
+class PathRegistry:
+    """Read paths from config + defaults and resolve relative to root."""
+
+    def __init__(self, root: Path, config: Optional[Dict] = None, create_dirs: bool = True):
+        self.root = Path(root).resolve()
+        self._create_dirs = create_dirs
+
+        # Merge defaults with config
+        merged_config = {}
+        config = config or {}
+        for section, defaults in DEFAULT_STRUCTURE.items():
+            section_cfg = config.get(section, {})
+            merged_section = dict(defaults)
+            # YAML overwrites defaults if present
+            merged_section.update(section_cfg)
+            merged_config[section] = merged_section
+        self.config = merged_config
+
+        # Validate merged config
+        validate_config_structure(self.config)
+
+        # Create sections dynamically
+        for section, keys in DEFAULT_STRUCTURE.items():
+            setattr(self, section, self._init_section(
+                section, self.config[section]))
+
+    def _init_section(self, section: str, section_config: Dict[str, str]) -> Dict[str, Path]:
+        """Resolve section paths and create directories."""
+        container: Dict[str, Path] = {}
+        for key, rel_path in section_config.items():
+            path = (self.root / rel_path).resolve()
+            if self._create_dirs:
+                path.mkdir(parents=True, exist_ok=True)
+            container[key] = path
+        return container
+
+
+class Settings:
+    """Main settings object exposing path sections as properties."""
+
+    def __init__(self, root: Optional[Path] = None, config: Optional[Dict] = None, create_dirs: bool = True):
+        self.root = Path(root).resolve() if root else get_project_root()
+        self.config = config or load_config()
+        self.paths = PathRegistry(
+            self.root, self.config, create_dirs=create_dirs)
+
+    @property
+    def DATA(self) -> Dict[str, Path]:
+        return self.paths.data
+
+    @property
+    def LOGS(self) -> Dict[str, Path]:
+        return self.paths.logs
+
+    @property
+    def REPORTS(self) -> Dict[str, Path]:
+        return self.paths.reports  # reports_dir is inside data
+
+    @property
+    def MODELS(self) -> Dict[str, Path]:
+        return self.paths.models
+
+    @property
+    def ARTIFACTS(self) -> Dict[str, Path]:
+        return self.paths.artifacts
+
+    @property
+    def DOCS(self) -> Dict[str, Path]:
+        return self.paths.docs
+
+    @property
+    def NOTEBOOKS(self) -> Dict[str, Path]:
+        return self.paths.notebooks
+
+    @property
+    def SCRIPTS(self) -> Dict[str, Path]:
+        return self.paths.scripts
+
+    @property
+    def TESTS(self) -> Dict[str, Path]:
+        return self.paths.tests
+
+
+# global settings instance
+settings = Settings()
diff --git a/src/insurance_analytics/core/scheduler.py b/src/insurance_analytics/core/scheduler.py
new file mode 100644
index 0000000..e69de29
diff --git a/src/insurance_analytics/eda/__init__.py b/src/insurance_analytics/eda/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/src/insurance_analytics/eda/exploration.py b/src/insurance_analytics/eda/exploration.py
new file mode 100644
index 0000000..4a76c1b
--- /dev/null
+++ b/src/insurance_analytics/eda/exploration.py
@@ -0,0 +1,74 @@
+# src/insurance_analytics/eda/exploration.py
+
+from pathlib import Path
+import pandas as pd
+import matplotlib.pyplot as plt
+import seaborn as sns
+from typing import Optional
+
+# -------------------------------------------------------------
+# 1) STRUCTURAL SUMMARY
+# -------------------------------------------------------------
+def dataset_overview(df: pd.DataFrame):
+    """
+    Returns basic overview: shape, column names, dtypes.
+    """
+    print("\n--- Dataset Shape ---")
+    print(df.shape)
+
+    print("\n--- Column Info ---")
+    print(df.dtypes)
+
+    print("\n--- Total Missing Values (%) ---")
+    print((df.isna().mean() * 100).sort_values(ascending=False).head(20))
+    df=df.head(5)
+    return df
+
+
+def duplicated_rows(df: pd.DataFrame):
+    """
+    Count duplicated rows in the dataset.
+    """
+    dup_count = df.duplicated().sum()
+    print(f"Duplicated rows: {dup_count}")
+    return dup_count
+# -------------------------------------------------------------
+# 2) MISSING DATA INSPECTION
+# -------------------------------------------------------------
+def summarize_missing(df: pd.DataFrame, top_n: Optional[int] = 20) -> pd.Series:
+    """
+    Compute the percentage of missing values per column and return the top N columns with highest missing rates.
+
+    Parameters
+    ----------
+    df : pd.DataFrame
+        The dataset to analyze.
+    top_n : int, optional
+        Number of top columns to return. Defaults to 20.
+
+    Returns
+    -------
+    pd.Series
+        Missing values as a fraction of total rows, sorted descending.
+    """
+    missing_pct = df.isna().mean().sort_values(ascending=False)
+    return missing_pct.head(top_n)
+# -------------------------------------------------------------
+# 3) OUTLIER INSPECTION
+# -------------------------------------------------------------
+def detect_outliers_iqr(df: pd.DataFrame, col: str):
+    """
+    Use IQR rule to detect outliers for a numeric column.
+    """
+    if col not in df.columns:
+        raise ValueError(f"{col} not found in DataFrame")
+
+    q1 = df[col].quantile(0.25)
+    q3 = df[col].quantile(0.75)
+    iqr = q3 - q1
+    lower = q1 - 1.5 * iqr
+    upper = q3 + 1.5 * iqr
+
+    outliers = df[(df[col] < lower) | (df[col] > upper)]
+    print(f"{col}: {len(outliers)} outliers detected")
+    return outliers
diff --git a/src/insurance_analytics/eda/visualization.py b/src/insurance_analytics/eda/visualization.py
new file mode 100644
index 0000000..e69de29
diff --git a/src/insurance_analytics/models/__init__.py b/src/insurance_analytics/models/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/src/insurance_analytics/models/evaluation.py b/src/insurance_analytics/models/evaluation.py
new file mode 100644
index 0000000..e69de29
diff --git a/src/insurance_analytics/models/interpretability.py b/src/insurance_analytics/models/interpretability.py
new file mode 100644
index 0000000..e69de29
diff --git a/src/insurance_analytics/models/linear_regression.py b/src/insurance_analytics/models/linear_regression.py
new file mode 100644
index 0000000..e69de29
diff --git a/src/insurance_analytics/models/random_forest.py b/src/insurance_analytics/models/random_forest.py
new file mode 100644
index 0000000..e69de29
diff --git a/src/insurance_analytics/models/xgboost_model.py b/src/insurance_analytics/models/xgboost_model.py
new file mode 100644
index 0000000..e69de29
diff --git a/src/insurance_analytics/preprocessing/__init__.py b/src/insurance_analytics/preprocessing/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/src/insurance_analytics/preprocessing/cleaner.py b/src/insurance_analytics/preprocessing/cleaner.py
new file mode 100644
index 0000000..1139c98
--- /dev/null
+++ b/src/insurance_analytics/preprocessing/cleaner.py
@@ -0,0 +1,113 @@
+# src/insurance_analytics/data/cleaner.py
+
+import pandas as pd
+import numpy as np
+from typing import List, Optional
+import re
+
+# -------------------------------------------------------------
+# 1) STRING CLEANING
+# -------------------------------------------------------------
+def clean_strings(df: pd.DataFrame) -> pd.DataFrame:
+    """
+    Trim whitespace, normalize empty values, and clean string anomalies.
+    """
+    str_cols = df.select_dtypes(include="object").columns
+
+    df[str_cols] = df[str_cols].apply(lambda col: col.str.strip())
+
+    df.replace({"": np.nan, " ": np.nan}, inplace=True)
+
+    # Replace weird zero strings (your custom rule)
+    df.replace({".000000000000": 0}, inplace=True)
+
+    return df
+
+
+# -------------------------------------------------------------
+# 2) DATE FIXING
+# -------------------------------------------------------------
+def fix_dates(df: pd.DataFrame, date_cols=None) -> pd.DataFrame:
+    """
+    Convert date columns using pandas. Handles both string dates and numeric epoch timestamps.
+    """
+    if date_cols is None:
+        date_cols = ["TransactionMonth"]
+
+    for col in date_cols:
+        if col in df.columns:
+            # If the column is integer-like (large numbers), treat as epoch in ns
+            if pd.api.types.is_integer_dtype(df[col]):
+                df[col] = pd.to_datetime(df[col], errors="coerce", unit="ns")
+            else:
+                df[col] = pd.to_datetime(df[col], errors="coerce")
+    return df
+
+
+# -------------------------------------------------------------
+# 3) NUMERIC FIXING
+# -------------------------------------------------------------
+def fix_numeric(df: pd.DataFrame) -> pd.DataFrame:
+    """
+    Convert numeric-like columns to numeric when possible.
+    """
+    for col in df.columns:
+        df[col] = pd.to_numeric(df[col], errors="ignore")
+    return df
+
+
+# -------------------------------------------------------------
+# 4) MISSING DATA INSPECTION
+# -------------------------------------------------------------
+def summarize_missing(df: pd.DataFrame, top_n: int = 20) -> pd.DataFrame:
+    """
+    Return the top N columns with the highest missing percentage.
+    """
+    missing_pct = df.isna().mean().sort_values(ascending=False)
+    return missing_pct.head(top_n)
+
+
+# -------------------------------------------------------------
+# 5) MISSING DATA CLEANUP
+# -------------------------------------------------------------
+def clean_missing(df: pd.DataFrame, drop_threshold: float = 0.85) -> pd.DataFrame:
+    """
+    Drop columns where more than `drop_threshold` fraction is missing.
+    """
+    missing_pct = df.isna().mean()
+    cols_to_drop = missing_pct[missing_pct > drop_threshold].index.tolist()
+
+    if cols_to_drop:
+        df = df.drop(columns=cols_to_drop)
+
+    return df
+
+
+
+# -------------------------------------------------------------
+# 6) FULL CLEANING PIPELINE
+# -------------------------------------------------------------
+def cleaning(
+    df: pd.DataFrame,
+    date_cols: Optional[List[str]] = None,
+    drop_threshold: float = 0.85,
+    report_missing: bool = True
+) -> pd.DataFrame:
+    """
+    Apply the standard cleaning pipeline: strings → dates → numeric → missing.
+    Optionally print or return the top missing-value columns.
+    """
+
+    # 1. clean string columns
+    df = clean_strings(df)
+
+    # 2. parse dates
+    df = fix_dates(df, date_cols=date_cols)
+
+    # 3. convert numeric-looking fields
+    df = fix_numeric(df)
+
+    # 5. drop excessively missing columns
+    df = clean_missing(df, drop_threshold=drop_threshold)
+
+    return df
diff --git a/src/insurance_analytics/preprocessing/feature_engineering.py b/src/insurance_analytics/preprocessing/feature_engineering.py
new file mode 100644
index 0000000..65e945c
--- /dev/null
+++ b/src/insurance_analytics/preprocessing/feature_engineering.py
@@ -0,0 +1,159 @@
+# src/insurance_analytics/features/feature_engineering.py
+
+import pandas as pd
+import numpy as np
+from sklearn.preprocessing import OneHotEncoder, StandardScaler
+from sklearn.compose import ColumnTransformer
+from sklearn.pipeline import Pipeline
+
+
+# -------------------------------------------------------------
+# 1) DATE FEATURES
+# -------------------------------------------------------------
+def add_date_features(df: pd.DataFrame, date_col: str = "TransactionMonth"):
+    """
+    Extract useful features from a datetime column.
+    """
+    if date_col not in df.columns:
+        return df
+
+    if not np.issubdtype(df[date_col].dtype, np.datetime64):
+        df[date_col] = pd.to_datetime(df[date_col], errors="coerce")
+
+    df["year"] = df[date_col].dt.year
+    df["month"] = df[date_col].dt.month
+    df["quarter"] = df[date_col].dt.quarter
+    df["is_month_start"] = df[date_col].dt.is_month_start.astype(int)
+    df["is_month_end"] = df[date_col].dt.is_month_end.astype(int)
+
+    return df
+
+
+# -------------------------------------------------------------
+# 2) NUMERIC BINNING (OPTIONAL)
+# -------------------------------------------------------------
+def bin_numeric(df: pd.DataFrame, col: str, bins: int = 5):
+    """
+    Create quantile-based bins for a numeric column.
+
+    Example: Age → Age_bin (5 bins)
+    """
+    if col not in df.columns:
+        return df
+
+    if not np.issubdtype(df[col].dtype, np.number):
+        return df
+
+    df[f"{col}_bin"] = pd.qcut(df[col], q=bins, duplicates="drop")
+    return df
+
+
+# -------------------------------------------------------------
+# 3) TARGET ENCODING (SAFE VERSION)
+# -------------------------------------------------------------
+def target_encode(df: pd.DataFrame, col: str, target: str):
+    """
+    Encode categorical column by mean of target.
+
+    Example: Bank → avg premium
+    """
+    if col not in df.columns or target not in df.columns:
+        return df
+
+    if np.issubdtype(df[target].dtype, np.number) is False:
+        return df
+
+    means = df.groupby(col)[target].mean()
+    df[f"{col}_te"] = df[col].map(means)
+    return df
+
+
+# -------------------------------------------------------------
+# 4) ONE-HOT ENCODING PIPELINE (for modeling)
+# -------------------------------------------------------------
+def prepare_preprocessor(df: pd.DataFrame):
+    """
+    Automatically build a ColumnTransformer:
+    - OneHotEncoder for categorical
+    - StandardScaler for numeric
+    """
+
+    numeric_cols = df.select_dtypes(
+        include=["int64", "float64"]).columns.tolist()
+    categorical_cols = df.select_dtypes(include="object").columns.tolist()
+
+    numeric_transformer = Pipeline(
+        steps=[
+            ("scaler", StandardScaler())
+        ]
+    )
+
+    categorical_transformer = Pipeline(
+        steps=[
+            ("onehot", OneHotEncoder(handle_unknown="ignore"))
+        ]
+    )
+
+    preprocessor = ColumnTransformer(
+        transformers=[
+            ("num", numeric_transformer, numeric_cols),
+            ("cat", categorical_transformer, categorical_cols),
+        ]
+    )
+
+    return preprocessor, numeric_cols, categorical_cols
+
+
+# -------------------------------------------------------------
+# 5) INTERACTION FEATURES
+# -------------------------------------------------------------
+def add_interactions(df: pd.DataFrame, pairs: list[tuple]):
+    """
+    Create interaction features through multiplication.
+
+    Example:
+        pairs = [
+            ("EngineCapacity", "VehicleAge"),
+            ("TotalPremium", "TotalClaims")
+        ]
+    """
+    for col1, col2 in pairs:
+        if col1 in df.columns and col2 in df.columns:
+            name = f"{col1}_x_{col2}"
+            df[name] = df[col1] * df[col2]
+
+    return df
+
+
+# -------------------------------------------------------------
+# 6) FEATURE SELECTION HOOK (for task 2 modeling)
+# -------------------------------------------------------------
+def remove_low_variance(df: pd.DataFrame, threshold: float = 0.0):
+    """
+    Drop columns that have zero or near-zero variance.
+    """
+
+    low_var_cols = [
+        col for col in df.columns
+        if df[col].nunique() <= 1
+    ]
+
+    df = df.drop(columns=low_var_cols)
+
+    return df
+
+# -------------------------------------------------------------
+# 7) LOSS RATIO
+# -------------------------------------------------------------
+
+
+def add_loss_ratio(df: pd.DataFrame, claims_col: str = "TotalClaims", premium_col: str = "TotalPremium"):
+    """
+    Add Loss Ratio column: LossRatio = TotalClaims / TotalPremium.
+    Handles zero or missing premiums safely.
+    """
+    if claims_col not in df.columns or premium_col not in df.columns:
+        return df
+
+    df["LossRatio"] = df[claims_col] / df[premium_col].replace({0: np.nan})
+    return df
diff --git a/src/insurance_analytics/utils/__init__.py b/src/insurance_analytics/utils/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/src/insurance_analytics/utils/io_utils.py b/src/insurance_analytics/utils/io_utils.py
new file mode 100644
index 0000000..da9c848
--- /dev/null
+++ b/src/insurance_analytics/utils/io_utils.py
@@ -0,0 +1,76 @@
+"""
+Utility functions for file input/output operations.
+Handles CSV and text files with safe error-handling.
+"""
+
+import os
+import pandas as pd
+
+
+def read_csv(path):
+    """
+    Safely read a CSV file.
+
+    Args:
+        path (str): File path to read.
+
+    Returns:
+        DataFrame | None: Loaded pandas DataFrame, or None on error.
+    """
+    try:
+        return pd.read_csv(path)
+    except FileNotFoundError:
+        print(f"[IO] File not found: {path}")
+    except Exception as e:
+        print(f"[IO] Failed to read CSV: {e}")
+    return None
+
+
+def write_csv(df, path):
+    """
+    Write a DataFrame to CSV safely.
+
+    Args:
+        df (DataFrame): The data to write.
+        path (str): Destination path.
+    """
+    try:
+        os.makedirs(os.path.dirname(path), exist_ok=True)
+        df.to_csv(path, index=False)
+        print(f"[IO] CSV file is saved sucessfully")
+    except Exception as e:
+        print(f"[IO] Failed to write CSV: {e}")
+
+
+def read_text(path):
+    """
+    Read text from a file.
+
+    Args:
+        path (str): Path to the text file.
+
+    Returns:
+        str | None: File contents or None on error.
+    """
+    try:
+        with open(path, "r") as f:
+            return f.read()
+    except Exception as e:
+        print(f"[IO] Error reading text: {e}")
+        return None
+
+
+def write_text(path, content):
+    """
+    Write text to a file.
+
+    Args:
+        path (str): Destination path.
+        content (str): Text to write.
+    """
+    try:
+        os.makedirs(os.path.dirname(path), exist_ok=True)
+        with open(path, "w") as f:
+            f.write(content)
+    except Exception as e:
+        print(f"[IO] Error writing text: {e}")
diff --git a/src/insurance_analytics/utils/metrics.py b/src/insurance_analytics/utils/metrics.py
new file mode 100644
index 0000000..6d2a9df
--- /dev/null
+++ b/src/insurance_analytics/utils/metrics.py
@@ -0,0 +1,61 @@
+"""
+Common statistical metrics used across analytics modules.
+"""
+
+import numpy as np
+
+
+def safe_mean(values):
+    """
+    Compute mean safely with error handling.
+
+    Args:
+        values (list | array): Numerical values.
+
+    Returns:
+        float | None: Mean of values.
+    """
+    try:
+        arr = np.array(values)
+        return float(np.nanmean(arr))
+    except Exception as e:
+        print(f"[METRICS] Mean failed: {e}")
+        return None
+
+
+def safe_std(values):
+    """
+    Compute standard deviation safely.
+
+    Args:
+        values (list | array): Numerical values.
+
+    Returns:
+        float | None: Standard deviation.
+    """
+    try:
+        arr = np.array(values)
+        return float(np.nanstd(arr))
+    except Exception as e:
+        print(f"[METRICS] STD failed: {e}")
+        return None
+
+
+def calculate_throughput(count, time_sec):
+    """
+    Calculate throughput = items / time.
+
+    Args:
+        count (int): Number of processed items.
+        time_sec (float): Time taken in seconds.
+
+    Returns:
+        float | None: Throughput result.
+    """
+    try:
+        if time_sec == 0:
+            return None
+        return count / time_sec
+    except Exception as e:
+        print(f"[METRICS] Throughput calc failed: {e}")
+        return None
diff --git a/src/insurance_analytics/utils/project_root.py b/src/insurance_analytics/utils/project_root.py
new file mode 100644
index 0000000..58f9c2a
--- /dev/null
+++ b/src/insurance_analytics/utils/project_root.py
@@ -0,0 +1,22 @@
+from pathlib import Path
+
+
+def get_project_root() -> Path:
+    """
+    Return the project root by climbing upward until a folder
+    containing known project markers is found.
+
+    This is safer than hardcoding parent levels and works even
+    if the file structure changes.
+    """
+    current = Path(__file__).resolve()
+
+    markers = {"configs", "src", "requirements.txt", "pyproject.toml"}
+
+    for parent in current.parents:
+        if any((parent / m).exists() for m in markers):
+            return parent
+
+    raise RuntimeError(
+        "Could not determine project root. No known project markers found."
+    )
diff --git a/src/insurance_analytics/utils/system.py b/src/insurance_analytics/utils/system.py
new file mode 100644
index 0000000..1e1b75c
--- /dev/null
+++ b/src/insurance_analytics/utils/system.py
@@ -0,0 +1,48 @@
+"""
+System resource utilities for CPU, memory, and process inspection.
+"""
+
+import psutil
+
+
+def get_cpu_usage():
+    """
+    Get CPU usage as a percentage.
+
+    Returns:
+        float | None: CPU usage percentage, or None on failure.
+    """
+    try:
+        return psutil.cpu_percent(interval=1)
+    except Exception as e:
+        print(f"[SYSTEM] CPU check failed: {e}")
+        return None
+
+
+def get_memory_usage():
+    """
+    Get memory usage statistics.
+
+    Returns:
+        dict | None: Memory info dictionary, or None on failure.
+    """
+    try:
+        mem = psutil.virtual_memory()
+        return {"total": mem.total, "used": mem.used, "percent": mem.percent}
+    except Exception as e:
+        print(f"[SYSTEM] Memory check failed: {e}")
+        return None
+
+
+def get_process_info():
+    """
+    Retrieve list of running processes.
+
+    Returns:
+        list: List of process dictionaries.
+    """
+    try:
+        return [{"pid": p.pid, "name": p.name()} for p in psutil.process_iter()]
+    except Exception as e:
+        print(f"[SYSTEM] Process list failed: {e}")
+        return []
diff --git a/src/insurance_analytics/utils/validation.py b/src/insurance_analytics/utils/validation.py
new file mode 100644
index 0000000..7d50222
--- /dev/null
+++ b/src/insurance_analytics/utils/validation.py
@@ -0,0 +1,33 @@
+# src/insurance_analytics/utils/validation.py
+from typing import Dict
+
+REQUIRED_KEYS = {
+    "data": ["data_dir", "raw_dir", "processed_dir"],
+    "logs": ["logs_dir"],
+    "reports": ["reports_dir", "plots_dir"],
+    "models": ["models_dir"],
+    "artifacts": ["artifacts_dir"],
+}
+
+
+def validate_config_structure(config: Dict) -> None:
+    """
+    Validate that required sections and keys exist in the config dict.
+
+    Raises ValueError if a required key is missing.
+    """
+    if not isinstance(config, dict):
+        raise ValueError("Config must be a mapping/dictionary.")
+
+    for section, keys in REQUIRED_KEYS.items():
+        section_data = config.get(section)
+        if section_data is None:
+            raise ValueError(f"Missing '{section}' section in data.yaml")
+
+        if not isinstance(section_data, dict):
+            raise ValueError(
+                f"Expected '{section}' section to be a mapping (dict).")
+
+        for key in keys:
+            if key not in section_data:
+                raise ValueError(f"Missing '{section}.{key}' in data.yaml")
diff --git a/src/insurance_analytics/viz/__init__.py b/src/insurance_analytics/viz/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/src/insurance_analytics/viz/plots.py b/src/insurance_analytics/viz/plots.py
new file mode 100644
index 0000000..648c130
--- /dev/null
+++ b/src/insurance_analytics/viz/plots.py
@@ -0,0 +1,417 @@
+# src/insurance_analytics/viz/plots.py
+
+import matplotlib.pyplot as plt
+import seaborn as sns
+from pathlib import Path
+import pandas as pd
+
+sns.set_theme(style="whitegrid")
+
+
+
+# -------------------------------------------------------------
+# 2) UNIVARIATE ANALYSIS
+# -------------------------------------------------------------
+def plot_histogram(df: pd.DataFrame, column: str, bins: int = 30, save_path: Path = None):
+    """
+    Plot a histogram for a numeric column.
+
+    Parameters
+    ----------
+    df : pd.DataFrame
+        Cleaned dataset.
+    column : str
+        Column name to plot.
+    bins : int
+        Number of bins in histogram.
+    save_path : Path, optional
+        Path to save the plot image.
+    """
+    plt.figure(figsize=(10, 5))
+    ax = sns.histplot(df[column].dropna(), bins=bins,
+                      kde=True, color="skyblue")
+    ax.set_title(f"Distribution of {column}")
+    ax.set_xlabel(column)
+    ax.set_ylabel("Frequency")
+    plt.tight_layout()
+
+    if save_path:
+        plt.savefig(save_path, dpi=300)
+    plt.show()
+
+
+def plot_bar(df: pd.DataFrame, column: str, top_n: int = 20, save_path: Path = None):
+    """
+    Plot a bar chart for categorical columns showing top N categories.
+
+    Parameters
+    ----------
+    df : pd.DataFrame
+        Cleaned dataset.
+    column : str
+        Categorical column to plot.
+    top_n : int
+        Show top N categories only.
+    save_path : Path, optional
+        Path to save the plot image.
+    """
+    counts = df[column].value_counts().nlargest(top_n)
+    plt.figure(figsize=(12, 6))
+    ax = sns.barplot(x=counts.index, y=counts.values, palette="pastel")
+    ax.set_title(f"Top {top_n} {column} Categories")
+    ax.set_xlabel(column)
+    ax.set_ylabel("Count")
+    plt.xticks(rotation=45)
+    plt.tight_layout()
+
+    if save_path:
+        plt.savefig(save_path, dpi=300)
+    plt.show()
+
+
+def missing_data_summary(df: pd.DataFrame, top_n: int = 20):
+    """
+    Show top N columns with the highest percentage of missing values.
+
+    Parameters
+    ----------
+    df : pd.DataFrame
+        Cleaned dataset.
+    top_n : int
+        Number of top missing columns to show.
+
+    Returns
+    -------
+    pd.Series
+        Percentage of missing values per column.
+    """
+    missing_pct = df.isna().mean().sort_values(ascending=False)
+    return missing_pct.head(top_n)
+
+
+def boxplot_numeric(df: pd.DataFrame, column: str, save_path: Path = None):
+    """
+    Boxplot to detect outliers in a numeric column.
+
+    Parameters
+    ----------
+    df : pd.DataFrame
+        Cleaned dataset.
+    column : str
+        Column to visualize.
+    save_path : Path, optional
+        Path to save the plot image.
+    """
+    plt.figure(figsize=(10, 5))
+    ax = sns.boxplot(x=df[column], color="lightcoral")
+    ax.set_title(f"Boxplot of {column}")
+    plt.tight_layout()
+
+    if save_path:
+        plt.savefig(save_path, dpi=300)
+    plt.show()
+
+
+
+# -------------------------------------------------------------
+# 4) BIVARIATE ANALYSIS
+# -------------------------------------------------------------
+
+
+def correlation_matrix(df: pd.DataFrame, numeric_cols=None, save_path: Path = None):
+    """
+    Compute and plot a correlation matrix heatmap for numeric columns.
+
+    Parameters
+    ----------
+    df : pd.DataFrame
+        Cleaned dataset.
+    numeric_cols : list, optional
+        List of numeric columns to include. If None, selects all numeric columns.
+    save_path : Path, optional
+        Path to save the plot image.
+    """
+    if numeric_cols is None:
+        numeric_cols = df.select_dtypes(include="number").columns.tolist()
+
+    corr = df[numeric_cols].corr()
+
+    plt.figure(figsize=(12, 10))
+    ax = sns.heatmap(corr, annot=True, fmt=".2f", cmap="coolwarm", cbar=True)
+    ax.set_title("Correlation Matrix")
+    plt.tight_layout()
+
+    if save_path:
+        plt.savefig(save_path, dpi=300)
+    plt.show()
+    return corr
+
+
+def scatter_by_group(
+    df: pd.DataFrame, x_col: str, y_col: str, hue_col: str, save_path: Path = None
+):
+    """
+    Scatter plot of two numeric variables, colored by a categorical variable.
+
+    Parameters
+    ----------
+    df : pd.DataFrame
+        Cleaned dataset.
+    x_col : str
+        Column for x-axis.
+    y_col : str
+        Column for y-axis.
+    hue_col : str
+        Column for color grouping.
+    save_path : Path, optional
+        Path to save the plot image.
+    """
+    plt.figure(figsize=(12, 6))
+    ax = sns.scatterplot(x=x_col, y=y_col, hue=hue_col,
+                         data=df, alpha=0.6, palette="tab10")
+    ax.set_title(f"{y_col} vs {x_col} by {hue_col}")
+    plt.tight_layout()
+
+    if save_path:
+        plt.savefig(save_path, dpi=300)
+    plt.show()
+
+
+def boxplot_by_category(df: pd.DataFrame, numeric_col: str, category_col: str, save_path: Path = None):
+    """
+    Boxplot to visualize distribution of a numeric variable across categories.
+
+    Parameters
+    ----------
+    df : pd.DataFrame
+        Cleaned dataset.
+    numeric_col : str
+        Column with numeric data.
+    category_col : str
+        Column with categorical grouping.
+    save_path : Path, optional
+        Path to save the plot image.
+    """
+    plt.figure(figsize=(12, 6))
+    ax = sns.boxplot(x=category_col, y=numeric_col, data=df, palette="pastel")
+    ax.set_title(f"{numeric_col} by {category_col}")
+    plt.xticks(rotation=45)
+    plt.tight_layout()
+
+    if save_path:
+        plt.savefig(save_path, dpi=300)
+    plt.show()
+
+
+def pairplot_numeric(df: pd.DataFrame, numeric_cols=None, hue_col=None, save_path: Path = None):
+    """
+    Pairplot for numeric columns to explore pairwise relationships.
+
+    Parameters
+    ----------
+    df : pd.DataFrame
+        Cleaned dataset.
+    numeric_cols : list, optional
+        Columns to include. Defaults to all numeric columns.
+    hue_col : str, optional
+        Categorical column for coloring.
+    save_path : Path, optional
+        Path to save the plot image.
+    """
+    if numeric_cols is None:
+        numeric_cols = df.select_dtypes(include="number").columns.tolist()
+
+    pairplot = sns.pairplot(
+        df[numeric_cols + ([hue_col] if hue_col else [])], hue=hue_col)
+    plt.tight_layout()
+
+    if save_path:
+        plt.savefig(save_path, dpi=300)
+    plt.show()
+
+def plot_loss_ratio_by_province(df: pd.DataFrame, save_path: Path = None):
+    """
+    Bar plot: Average LossRatio by Province.
+
+    Parameters
+    ----------
+    df : pd.DataFrame
+        Cleaned insurance dataset with 'Province' and 'LossRatio' columns.
+    save_path : Path, optional
+        Path to save the plot image.
+    """
+    plt.figure(figsize=(12, 6))
+    ax = sns.barplot(
+        x="Province",
+        y="LossRatio",
+        data=df.groupby("Province")["LossRatio"].mean().reset_index(),
+        palette="Blues_r"
+    )
+    ax.set_title("Average Loss Ratio by Province")
+    ax.set_ylabel("Loss Ratio")
+    ax.set_xlabel("Province")
+    plt.xticks(rotation=45, ha="right")
+    plt.tight_layout()
+
+    if save_path:
+        plt.savefig(save_path, dpi=300)
+    plt.show()
+
+
+def plot_loss_ratio_distribution(df: pd.DataFrame, save_path: Path = None):
+    """
+    Histogram + KDE: Distribution of LossRatio.
+
+    Parameters
+    ----------
+    df : pd.DataFrame
+        Cleaned insurance dataset with 'LossRatio'.
+    save_path : Path, optional
+        Path to save the plot image.
+    """
+    plt.figure(figsize=(10, 5))
+    sns.histplot(df["LossRatio"].dropna(), bins=50, kde=True, color="orange")
+    plt.title("Distribution of Loss Ratio")
+    plt.xlabel("Loss Ratio")
+    plt.ylabel("Frequency")
+    plt.tight_layout()
+
+    if save_path:
+        plt.savefig(save_path, dpi=300)
+    plt.show()
+
+
+def plot_claims_vs_premium_by_vehicle(df: pd.DataFrame, save_path: Path = None):
+    """
+    Scatter plot: TotalClaims vs TotalPremium colored by VehicleType.
+
+    Parameters
+    ----------
+    df : pd.DataFrame
+        Cleaned insurance dataset with 'TotalClaims', 'TotalPremium', 'VehicleType'.
+    save_path : Path, optional
+        Path to save the plot image.
+    """
+    plt.figure(figsize=(12, 6))
+    sns.scatterplot(
+        x="TotalPremium",
+        y="TotalClaims",
+        hue="VehicleType",
+        data=df,
+        alpha=0.6,
+        palette="tab10"
+    )
+    plt.title("Total Claims vs Total Premium by Vehicle Type")
+    plt.xlabel("Total Premium")
+    plt.ylabel("Total Claims")
+    plt.legend(title="Vehicle Type", bbox_to_anchor=(
+        1.05, 1), loc="upper left")
+    plt.tight_layout()
+
+    if save_path:
+        plt.savefig(save_path, dpi=300)
+    plt.show()
+
+# src/insurance_analytics/viz/outliers.py
+
+
+sns.set_theme(style="whitegrid")
+
+# -------------------------------------------------------------
+# Boxplot for Outlier Detection
+# -------------------------------------------------------------
+
+
+def boxplot_outliers(df: pd.DataFrame, column: str, save_path: Path = None):
+    """
+    Visualize outliers in a numeric column using a boxplot.
+
+    Parameters
+    ----------
+    df : pd.DataFrame
+        Cleaned dataset
+    column : str
+        Column name to visualize
+    save_path : Path, optional
+        Path to save the plot image
+    """
+    plt.figure(figsize=(10, 6))
+    ax = sns.boxplot(x=df[column], color="lightcoral")
+    ax.set_title(f"Boxplot of {column} (Outlier Detection)")
+    ax.set_xlabel(column)
+    plt.tight_layout()
+
+    if save_path:
+        plt.savefig(save_path, dpi=300)
+    plt.show()
+
+
+# -------------------------------------------------------------
+# Scatterplot to visualize outliers relative to another variable
+# -------------------------------------------------------------
+def scatter_outliers(df: pd.DataFrame, x_col: str, y_col: str, hue_col: str = None, save_path: Path = None):
+    """
+    Scatter plot highlighting potential outliers relative to another numeric variable.
+
+    Parameters
+    ----------
+    df : pd.DataFrame
+        Cleaned dataset
+    x_col : str
+        Column for x-axis
+    y_col : str
+        Column for y-axis
+    hue_col : str, optional
+        Column to color points by category
+    save_path : Path, optional
+        Path to save the plot image
+    """
+    plt.figure(figsize=(12, 6))
+    ax = sns.scatterplot(x=x_col, y=y_col, hue=hue_col,
+                         data=df, palette="tab10", alpha=0.6)
+    ax.set_title(f"{y_col} vs {x_col} (Outlier Inspection)")
+    plt.tight_layout()
+
+    if save_path:
+        plt.savefig(save_path, dpi=300)
+    plt.show()
+
+
+# -------------------------------------------------------------
+# IQR-based Outlier Highlighting
+# -------------------------------------------------------------
+def highlight_outliers_iqr(df: pd.DataFrame, column: str, save_path: Path = None):
+    """
+    Highlight points outside the IQR range for a numeric column.
+
+    Parameters
+    ----------
+    df : pd.DataFrame
+        Cleaned dataset
+    column : str
+        Column name for outlier detection
+    save_path : Path, optional
+        Path to save the plot
+    """
+    q1 = df[column].quantile(0.25)
+    q3 = df[column].quantile(0.75)
+    iqr = q3 - q1
+    lower = q1 - 1.5 * iqr
+    upper = q3 + 1.5 * iqr
+
+    df['is_outlier'] = (df[column] < lower) | (df[column] > upper)
+
+    plt.figure(figsize=(12, 6))
+    ax = sns.scatterplot(x=df.index, y=df[column], hue=df['is_outlier'], palette={
+                         True: 'red', False: 'blue'})
+    ax.set_title(f"Outlier Highlighting for {column}")
+    ax.set_xlabel("Index")
+    ax.set_ylabel(column)
+    plt.tight_layout()
+
+    if save_path:
+        plt.savefig(save_path, dpi=300)
+    plt.show()
+
+    # Remove temporary column
+    df.drop(columns=['is_outlier'], inplace=True)
diff --git a/src/models/train_model.py b/src/models/train_model.py
deleted file mode 100644
index 2713167..0000000
--- a/src/models/train_model.py
+++ /dev/null
@@ -1,19 +0,0 @@
-from sklearn.model_selection import train_test_split
-from sklearn.ensemble import RandomForestRegressor
-from sklearn.metrics import mean_squared_error,r2_score
-
-def train_model(df, target):
-    X = df.drop(columns=[target])
-    y = df[target]
-
-    X_train,X_test,y_train,y_test = train_test_split(X,y,test_size=0.2)
-
-    model = RandomForestRegressor()
-    model.fit(X_train,y_train)
-
-    preds = model.predict(X_test)
-
-    rmse = mean_squared_error(y_test,preds,squared=False)
-    r2 = r2_score(y_test,preds)
-
-    return model,rmse,r2
diff --git a/src/utils/__pycache__/data_loader.cpython-311.pyc b/src/utils/__pycache__/data_loader.cpython-311.pyc
deleted file mode 100644
index 4132a51..0000000
Binary files a/src/utils/__pycache__/data_loader.cpython-311.pyc and /dev/null differ
diff --git a/src/utils/data_loader.py b/src/utils/data_loader.py
deleted file mode 100644
index 19cfeba..0000000
--- a/src/utils/data_loader.py
+++ /dev/null
@@ -1,19 +0,0 @@
-# src/utils/data_loader.py
-import pandas as pd
-from typing import Optional
-
-def load_csv(path: str, parse_dates: Optional[list] = None) -> pd.DataFrame:
-    """
-    Load CSV into a DataFrame.
-    - path: file path
-    - parse_dates: list of column names to parse as dates
-    """
-    df = pd.read_csv(path, low_memory=False)
-    if parse_dates:
-        for c in parse_dates:
-            if c in df.columns:
-                df[c] = pd.to_datetime(df[c], errors="coerce")
-    return df
-
-def save_csv(df: pd.DataFrame, path: str):
-    df.to_csv(path, index=False)
diff --git a/tests/__init__.py b/tests/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/tests/integration/__init__.py b/tests/integration/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/tests/integration/test_dvc_integration.py b/tests/integration/test_dvc_integration.py
new file mode 100644
index 0000000..e69de29
diff --git a/tests/integration/test_eda_pipeline.py b/tests/integration/test_eda_pipeline.py
new file mode 100644
index 0000000..e69de29
diff --git a/tests/integration/test_full_pipeline.py b/tests/integration/test_full_pipeline.py
new file mode 100644
index 0000000..e69de29
diff --git a/tests/integration/test_model_pipeline.py b/tests/integration/test_model_pipeline.py
new file mode 100644
index 0000000..e69de29
diff --git a/tests/test_data_loader.py b/tests/test_data_loader.py
deleted file mode 100644
index b9e4af7..0000000
--- a/tests/test_data_loader.py
+++ /dev/null
@@ -1,4 +0,0 @@
-from src.utils.data_loader import load_data
-
-def test_loader():
-    assert load_data
diff --git a/tests/unit/__init__.py b/tests/unit/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/tests/unit/test_cleaners.py b/tests/unit/test_cleaners.py
new file mode 100644
index 0000000..e69de29
diff --git a/tests/unit/test_features.py b/tests/unit/test_features.py
new file mode 100644
index 0000000..e69de29
diff --git a/tests/unit/test_hypothesis.py b/tests/unit/test_hypothesis.py
new file mode 100644
index 0000000..e69de29
diff --git a/tests/unit/test_loaders.py b/tests/unit/test_loaders.py
new file mode 100644
index 0000000..e69de29
diff --git a/tests/unit/test_models.py b/tests/unit/test_models.py
new file mode 100644
index 0000000..e69de29
diff --git a/tests/unit/test_registy.py b/tests/unit/test_registy.py
new file mode 100644
index 0000000..2aa4608
--- /dev/null
+++ b/tests/unit/test_registy.py
@@ -0,0 +1,163 @@
+import tempfile
+import unittest
+from pathlib import Path
+from unittest import mock
+
+from insurance_analytics.core.registry import PathRegistry, Settings
+
+# The config you provided (kept as a Python dict)
+SAMPLE_CONFIG = {
+    "data": {
+        "data_dir": "data",
+        "raw_dir": "data/raw",
+        "interim_dir": "data/interim",
+        "processed_dir": "data/processed",
+        "postgres_exports_dir": "data/postgres_exports",
+    },
+    "logs": {"logs_dir": "logs"},
+    "reports": {"reports_dir": "reports", "plots_dir": "plots"},
+    "models": {"models_dir": "src/insurance_analytics/models"},
+    "artifacts": {"artifacts_dir": "artifacts"},
+}
+
+
+class TestRegistryWithSampleConfig(unittest.TestCase):
+    def setUp(self):
+        # Temporary project root for each test
+        self._tmpdir = tempfile.TemporaryDirectory()
+        self.project_root = Path(self._tmpdir.name) / "project_root"
+        self.project_root.mkdir(parents=True, exist_ok=True)
+
+        # Patch validate_config_structure so tests don't depend on its implementation
+        self._validate_patcher = mock.patch(
+            "insurance_analytics.core.registry.validate_config_structure"
+        )
+        self.mock_validate = self._validate_patcher.start()
+
+    def tearDown(self):
+        self._validate_patcher.stop()
+        self._tmpdir.cleanup()
+
+    def test_all_paths_resolved_and_created(self):
+        """Using SAMPLE_CONFIG -> every configured path should be resolved and directory created."""
+        registry = PathRegistry(
+            self.project_root, SAMPLE_CONFIG, create_dirs=True)
+
+        # Data section keys
+        for key, rel in SAMPLE_CONFIG["data"].items():
+            self.assertIn(key, registry.data,
+                          f"{key} missing from registry.data")
+            expected = (self.project_root / rel).resolve()
+            self.assertEqual(registry.data[key], expected)
+            self.assertTrue(expected.exists() and expected.is_dir())
+
+        # Logs
+        self.assertIn("logs_dir", registry.logs)
+        expected_logs = (self.project_root /
+                         SAMPLE_CONFIG["logs"]["logs_dir"]).resolve()
+        self.assertEqual(registry.logs["logs_dir"], expected_logs)
+        self.assertTrue(expected_logs.exists() and expected_logs.is_dir())
+
+        # Reports
+        for key, rel in SAMPLE_CONFIG["reports"].items():
+            self.assertIn(key, registry.reports)
+            expected = (self.project_root / rel).resolve()
+            self.assertEqual(registry.reports[key], expected)
+            self.assertTrue(expected.exists() and expected.is_dir())
+
+        # Models
+        self.assertIn("models_dir", registry.models)
+        expected_models = (self.project_root /
+                           SAMPLE_CONFIG["models"]["models_dir"]).resolve()
+        self.assertEqual(registry.models["models_dir"], expected_models)
+        self.assertTrue(expected_models.exists() and expected_models.is_dir())
+
+        # Artifacts
+        self.assertIn("artifacts_dir", registry.artifacts)
+        expected_artifacts = (
+            self.project_root / SAMPLE_CONFIG["artifacts"]["artifacts_dir"]).resolve()
+        self.assertEqual(
+            registry.artifacts["artifacts_dir"], expected_artifacts)
+        self.assertTrue(expected_artifacts.exists()
+                        and expected_artifacts.is_dir())
+
+    def test_no_creation_when_create_dirs_false(self):
+        """When create_dirs=False, the Path objects should still resolve but directories shouldn't be created."""
+        registry = PathRegistry(
+            self.project_root, SAMPLE_CONFIG, create_dirs=False)
+
+        # Pick a subset to verify (if this passes for one, others are same logic)
+        expected_raw = (self.project_root /
+                        SAMPLE_CONFIG["data"]["raw_dir"]).resolve()
+        expected_models = (self.project_root /
+                           SAMPLE_CONFIG["models"]["models_dir"]).resolve()
+
+        self.assertEqual(registry.data["raw_dir"], expected_raw)
+        self.assertEqual(registry.models["models_dir"], expected_models)
+
+        self.assertFalse(expected_raw.exists(),
+                         "data/raw should not exist when create_dirs=False")
+        self.assertFalse(expected_models.exists(),
+                         "models dir should not exist when create_dirs=False")
+
+    def test_validate_config_structure_called_with_sample_config(self):
+        _ = PathRegistry(self.project_root, SAMPLE_CONFIG, create_dirs=False)
+        self.mock_validate.assert_called_once_with(SAMPLE_CONFIG)
+
+
+class TestSettingsUsingSampleConfig(unittest.TestCase):
+    def setUp(self):
+        # prepare temp project root and patch get_project_root to return it
+        self._tmpdir = tempfile.TemporaryDirectory()
+        self.project_root = Path(self._tmpdir.name) / "root"
+        self.project_root.mkdir(parents=True, exist_ok=True)
+
+        self._validate_patcher = mock.patch(
+            "insurance_analytics.core.registry.validate_config_structure"
+        )
+        self._validate_patcher.start()
+
+        self._get_root_patcher = mock.patch(
+            "insurance_analytics.core.registry.get_project_root", return_value=self.project_root
+        )
+        self.mock_get_root = self._get_root_patcher.start()
+
+    def tearDown(self):
+        self._get_root_patcher.stop()
+        self._validate_patcher.stop()
+        self._tmpdir.cleanup()
+
+    def test_settings_exposes_properties_and_resolves_paths(self):
+        settings = Settings(config=SAMPLE_CONFIG, create_dirs=True)
+
+        # Ensure properties map to underlying PathRegistry dicts
+        self.assertIs(settings.DATA, settings.paths.data)
+        self.assertIs(settings.LOGS, settings.paths.logs)
+        self.assertIs(settings.REPORTS, settings.paths.reports)
+        self.assertIs(settings.MODELS, settings.paths.models)
+        self.assertIs(settings.ARTIFACTS, settings.paths.artifacts)
+
+        # Verify at least one entry per section is correct
+        self.assertIn("data_dir", settings.DATA)
+        self.assertEqual(settings.DATA["data_dir"],
+                         (self.project_root / "data").resolve())
+
+        self.assertIn("logs_dir", settings.LOGS)
+        self.assertEqual(settings.LOGS["logs_dir"],
+                         (self.project_root / "logs").resolve())
+
+        self.assertIn("reports_dir", settings.REPORTS)
+        self.assertEqual(
+            settings.REPORTS["reports_dir"], (self.project_root / "reports").resolve())
+
+        self.assertIn("models_dir", settings.MODELS)
+        self.assertEqual(settings.MODELS["models_dir"], (
+            self.project_root / "src/insurance_analytics/models").resolve())
+
+        self.assertIn("artifacts_dir", settings.ARTIFACTS)
+        self.assertEqual(
+            settings.ARTIFACTS["artifacts_dir"], (self.project_root / "artifacts").resolve())
+
+
+if __name__ == "__main__":
+    unittest.main()