AI/ML Internship Mini Projects

This repository contains 4 mini AI/ML projects completed during my internship.
Each project includes datasets and Jupyter Notebooks for training and testing ML models.

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📌 Projects

1. Breast Cancer Detection

• Goal: Predict whether a tumor is malignant or benign.
• Techniques Used: Logistic Regression, Random Forest
• Dataset: Breast Cancer Wisconsin Dataset (data.csv)

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2. IPL Winning Team Prediction

• Goal: Predict the winning team based on past IPL match data.
• Techniques Used: Decision Tree, Random Forest
• Dataset: IPL match dataset (ipl_data.csv)

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3. Car Resale Price Prediction

• Goal: Predict the resale value of used cars based on age, mileage, fuel type, etc.
• Techniques Used: Linear Regression, Ridge Regression
• Dataset: Used car dataset (car_data.csv)

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4. Diabetes Prediction

• Goal: Predict whether a patient is likely to have diabetes.
• Techniques Used: Support Vector Machine (SVM), Logistic Regression
• Dataset: Pima Indians Diabetes Dataset (diabetes.xls)

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🚀 Tech Stack

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• Google Colab

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📂 Folder Structure

AI-ML-Internship-Projects/
│── README.md # Project overview
│── requirements.txt # Python dependencies
│
├── Breast_Cancer_Detection/ │ ├── data.csv
│ └── Breast_Cancer_Detection.ipynb
│
├── IPL_Winning_Team_Prediction/
│ ├── ipl_data.csv
│ └── IPL_Winning_Team_Prediction.ipynb
│
├── Car_Resale_Price_Prediction/
│ ├── car_data.csv
│ └── Car_Resale_Price_Prediction.ipynb
│
└── Diabetes_Prediction/
│ ├── diabetes.xls
│ └── Diabetes_Prediction.ipynb

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