Machine Learning Projects

This repository showcases multiple end-to-end Machine Learning and Data Science projects, covering domains such as healthcare, retail, entertainment, and customer analytics.
Each project demonstrates data storytelling, feature engineering, predictive modeling, and visualization techniques.

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📂 Project Structure

├── Titanic_Survival_Analysis/ ├── Netflix_Content_Strategy/ ├── Housing_Market_Prediction/ ├── Heart_Disease_Predictor/ ├── Customer_Segmentation/ ├── Store_Sales_Prediction/ ├── Customer_Churn_Prediction/ └── README.md

Each folder contains:

• Dataset (or a link to the dataset)
• Jupyter Notebook with step-by-step implementation
• Results & Visualizations
• Model Files (if applicable)

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Projects Overview

1️⃣ Data Storytelling: Analysing Survival on the Titanic

• Objective: Predict passenger survival using demographic and travel data.
• Techniques: Data cleaning, feature engineering (titles, family size), logistic regression, decision trees.
• Outcome: Insights into how gender, age, and class influenced survival rates.
• Folder: Titanic_Survival_Analysis/

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2️⃣ Cracking the Code: An Inside Look at Netflix's Content Strategy

• Objective: Analyze Netflix’s dataset to uncover trends in genres, regions, and release timelines.
• Techniques: EDA, clustering, data visualization.
• Outcome: Storytelling of Netflix’s content evolution and audience preferences.
• Folder: Netflix_Content_Strategy/

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3️⃣ Predicting Housing Market Trends with AI

• Objective: Build regression models to predict house prices.
• Techniques: Polynomial regression, random forest, gradient boosting.
• Outcome: Predictive model to assist in market trend analysis.
• Folder: Housing_Market_Prediction/

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4️⃣ AI in Healthcare: Building a Life-Saving Heart Disease Predictor

• Objective: Predict the likelihood of heart disease in patients.
• Techniques: Logistic regression, SVM, random forest, hyperparameter tuning.
• Outcome: Classification model as a decision-support tool for healthcare.
• Folder: Heart_Disease_Predictor/

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5️⃣ Smart Segmentation: Unlocking Customer Personas with AI

• Objective: Segment customers into groups for targeted marketing.
• Techniques: K-means clustering, PCA, visualization.
• Outcome: Personas identified for personalized marketing.
• Folder: Customer_Segmentation/

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6️⃣ Predicting Future Store Sales with AI

• Objective: Forecast sales for retail stores using historical data.
• Techniques: Time-series forecasting (ARIMA, Prophet), regression models.
• Outcome: Better demand planning and inventory management.
• Folder: Store_Sales_Prediction/

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7️⃣ Preventing Customer Churn with Feature Transformation

• Objective: Predict customer churn and identify retention strategies.
• Techniques: Feature transformation, ensemble models (XGBoost, Random Forest).
• Outcome: Early identification of at-risk customers to reduce churn.
• Folder: Customer_Churn_Prediction/

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Tech Stack

• Languages: Python
• Libraries: pandas, numpy, matplotlib, seaborn, scikit-learn, xgboost, statsmodels
• Visualization: Seaborn, Matplotlib, Plotly
• Notebooks: Jupyter / Google Colab

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Getting Started

1. Clone this repository:

   git clone https://github.com/chirustark17/Machine-Learning.git
   cd Machine-Learning
   
   

Results & Insights

Delivered predictive models across multiple industries.

Demonstrated EDA and storytelling for better data-driven insights.

Showcased importance of feature engineering and transformations.

Future Enhancements

Deploy selected models as interactive web apps (Streamlit/Flask).

Explore deep learning methods for healthcare and time series.

Automate pipeline workflows using MLflow.

Author

Developed by [Chirag K S] 📧 Email: K=kschirag969@gmail.com chiruchirag2447@gmail.com