📊 Exploratory Data Analysis Projects

1️⃣ Google Play Store

Performed data cleaning, feature engineering, and detailed EDA to analyze app ratings, installs, pricing trends, and category distributions.

2️⃣ Red Wine Dataset

Explored physicochemical properties of wine and analyzed their relationship with wine quality using statistical and visual techniques.

3️⃣ Flight Price Dataset

Performed feature engineering on date-time features and analyzed factors affecting flight prices.

4️⃣ Chronic Kidney Disease Dataset

Cleaned medical data, handled missing values, encoded categorical variables, and explored health indicators related to disease prediction.

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📊 EDA - 1 (Google Play Store Data)

Welcome to my first Exploratory Data Analysis (EDA) project! In this notebook, I have performed a detailed EDA on the Google Play Store dataset, uncovering key insights and cleaning the data for further machine learning tasks.

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📁 Dataset Overview

The dataset contains information about apps available on the Google Play Store, including:

• App name
• Category
• Rating
• Number of installs
• Type (Free/Paid)
• Price
• Size
• Content rating
• Genres
• Last updated
• Current version
• Android version

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🛠️ Tasks Performed

✅ Data Cleaning

• Handled missing values
• Fixed data types
• Cleaned columns like Size, Installs, and Price
• Removed duplicates and invalid entries

✅ Univariate Analysis

• Distribution of app ratings
• Most common app categories
• Distribution of app sizes and installs

✅ Bivariate/Multivariate Analysis

• Relationship between app rating and number of installs
• Price comparison across categories
• Category-wise average ratings

✅ Outlier Detection & Handling

• Identified and capped/remediated outliers in numerical columns

✅ Visualizations

• Histograms, box plots, scatter plots, bar charts
• Heatmaps for correlation matrix

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📌 Key Insights

• Free apps dominate the Play Store.
• Most apps have a rating between 4.0 and 4.5.
• Categories like Games and Tools are the most populated.
• Paid apps tend to have slightly higher ratings.
• App size does not strongly correlate with rating.

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📎 Tools Used

• Python (Pandas, NumPy, Matplotlib, Seaborn)
• Jupyter Notebook

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🍷 EDA - 2 (Red Wine Quality Dataset)

Welcome to my second Exploratory Data Analysis (EDA) project! In this notebook, I have analyzed an Red Wine Quality Dataset In this project, I analyzed the Red Wine Quality dataset to understand chemical properties affecting wine quality.

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📁 Dataset Overview

The dataset contains physicochemical properties such as:

• Fixed acidity
• Volatile acidity
• Citric acid
• Residual sugar
• Chlorides
• Sulphates
• Alcohol
• pH
• Quality (target variable)

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🛠️ Tasks Performed

• Checked data distribution
• Analyzed correlation between features
• Identified influential features affecting wine quality
• Performed outlier analysis
• Generated correlation heatmaps
• Compared feature distributions across quality levels

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📌 Key Insights

• Alcohol content positively correlates with wine quality.
• Volatile acidity negatively impacts quality.
• Some features show strong inter-correlation.
• The dataset has class imbalance in quality ratings.

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📎 Tools Used

• Python (Pandas, NumPy, Matplotlib, Seaborn)
• Jupyter Notebook

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📊 EDA - 3 (Chronic Disease Dataset)

Welcome to my third Exploratory Data Analysis (EDA) project! In this notebook, I have worked with a Chronic Disease dataset, analyzing patient-related features to discover patterns and potential indicators related to chronic conditions.

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📁 Dataset Overview

The dataset includes health and lifestyle-related information such as:

• Age
• Gender
• Blood Pressure
• Cholesterol Level
• Glucose Level
• Smoking, Alcohol, Physical Activity
• BMI
• Presence of Chronic Disease (Target Variable)

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🛠️ Tasks Performed

✅ Data Cleaning

• Checked for missing values and handled them
• Verified data types and converted where necessary
• Removed or corrected inconsistent records

✅ Univariate Analysis

• Distribution of numerical variables (e.g., age, BMI)
• Frequency of categorical features (e.g., gender, smoking status)
• Count of patients with and without chronic disease

✅ Bivariate Analysis

• Relationship between lifestyle habits and chronic disease
• Distribution of disease status across age groups
• Impact of glucose and cholesterol levels on health condition

✅ Multivariate Analysis

• Combined multiple features (e.g., age, lifestyle, medical history)
• Heatmap and pairplots to identify key patterns
• Correlation between features influencing chronic disease

✅ Visualizations

• Histograms, bar plots, count plots
• Box plots and violin plots for distributions
• Heatmaps and scatter matrices

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📌 Key Insights

• Higher glucose and cholesterol levels are common among chronic disease patients
• Lifestyle habits like smoking and lack of physical activity contribute to chronic conditions
• Certain age groups show higher disease prevalence
• Gender differences are notable in some health indicators

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📎 Tools Used

• Python (Pandas, NumPy, Matplotlib, Seaborn)
• Jupyter Notebook

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✈️ EDA - 4 (Flight Price Dataset)

Welcome to my fourth Exploratory Data Analysis (EDA) project! In this notebook, I have worked with a Flight Price Dataset This project focuses on airline ticket price prediction data.

📁 Dataset Overview

The dataset includes information such as:

• Airline
• Date of Journey
• Source
• Destination
• Duration
• Total Stops
• Additional Info
• Price (target)

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🛠️ Tasks Performed

✅ Data Cleaning

• Converted duration into minutes
• Extracted journey day/month
• Cleaned categorical features
• Handled missing values

✅ Feature Engineering

• Created time-based features=
• Encoded categorical variables
• Converted stops to numerical format

✅ EDA

• Airline-wise price comparison
• Source-destination analysis
• Price variation based on stops
• Duration vs price relationship

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📌 Key Insights

• Flights with more stops are generally cheaper.
• Airline brand significantly impacts price.
• Duration moderately correlates with price.
• Certain routes are consistently expensive.

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📎 Tools Used

• Python (Pandas, NumPy, Matplotlib, Seaborn)
• Jupyter Notebook

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🎯 Skills Demonstrated

• Data Cleaning
• Handling Missing Values
• Outlier Detection
• Feature Engineering
• Data Visualization
• Correlation Analysis
• Insight Extraction
• Preparing data for ML

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📌Future Improvements

• Add ML models after EDA
• Build Streamlit dashboards
• Deploy insights as web apps