📊 Machine Learning Projects

This repository contains multiple machine learning projects I have worked on. Each project applies data analysis, feature engineering, and ML models to solve real-world problems.

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📌 Projects Included

🎬 Movie Recommender (Content-Based)

• Goal: Recommend movies/shows similar to a given title or based on user description.
• Approach:
  • Data cleaning and preprocessing (description, genres, cast).
  • TF-IDF vectorization of text fields.
  • Feature fusion with weights.
  • Cosine similarity for recommendations.
• Features:
  • Recommend by movie title (if present in dataset).
  • Recommend by free-text query (e.g., “romantic comedy in New York”).
• Libraries: pandas, numpy, scikit-learn, scipy, matplotlib, seaborn.

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🚖 Uber Data Analysis

• Goal: Analyze Uber ride data to find trends and insights.
• Tasks:
  • Data cleaning and preprocessing.
  • Exploratory Data Analysis (EDA): trips by day, hour, and location.
  • Visualizations: heatmaps, bar plots, line plots.
  • Feature extraction for demand forecasting.
• Libraries: pandas, numpy, matplotlib, seaborn.

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❤️ Heart Attack Analysis and Prediction

• Goal: Predict the likelihood of a heart attack based on patient data.
• Dataset: Heart disease dataset with attributes such as age, cholesterol, blood pressure, etc.
• Approach:
  • Data preprocessing and handling missing values.
  • Feature scaling and selection.
  • Classification models: Logistic Regression, Decision Tree, Random Forest, SVM.
  • Model evaluation with accuracy, precision, recall, confusion matrix.
• Libraries: pandas, numpy, scikit-learn, matplotlib, seaborn.

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