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🚀 Startup Success Prediction

This repository contains a data mining project developed as part of the IN015 - Data Mining course.
The goal is to predict startup success using real-world data and machine learning techniques.

📂 Project Overview

Startups are inherently risky, but certain indicators—such as social media activity, engagement ratios, and web presence—may help assess their probability of success.
In this project, we:

  • Performed data cleaning and preprocessing on a dataset of 700 startups.
  • Handled missing values with domain-based imputations.
  • Applied exploratory data analysis (EDA), correlation heatmaps, and visualizations.
  • Built and compared multiple machine learning models:
    • Dummy Classifier (baseline)
    • Logistic Regression
    • Decision Tree
    • Random Forest
    • XGBoost

Our best-performing model was Random Forest, achieving ~88.9% precision in predicting successful startups.

🛠️ Tech Stack

  • Languages: Python 3
  • Libraries:
    • Data Handling: pandas, numpy
    • Visualization: matplotlib, seaborn
    • Machine Learning: scikit-learn, xgboost
    • Evaluation: metrics, cross-validation

📊 Results

  • Dummy Classifier: Very poor (predicts all startups fail).
  • Logistic Regression: ~75% precision.
  • Decision Tree: Weak performance (overfits easily).
  • Random Forest: Best model with 88.9% precision.
  • XGBoost: Promising results, comparable to Random Forest.