Project Title

🎯 Data Science Internship Tasks

Welcome to the Data Science Internship repository! 🚀 This repository contains hands-on tasks designed to enhance skills in exploratory data analysis, machine learning, and data processing.

📌 Tasks Overview

📝 Task 1: Exploratory Data Analysis (EDA) & Visualization

🚀 Titanic Dataset - Exploratory Data Analysis (EDA)

This project performs Exploratory Data Analysis (EDA) on the Titanic dataset to uncover key insights.

🔹 Features:

• ✅ Data Cleaning (handling missing values, outliers)
• ✅ Interactive Visualizations (histograms, bar charts)
• ✅ Correlation Analysis (heatmaps)
• ✅ Widget-based Passenger Filtering

📊 Interactive Notebook:

📌 Key Insights

• Most passengers traveled in 3rd class (budget-friendly).
• Majority of passengers were aged 20-30 years.
• Higher fares are positively correlated with survival.
• Missing values were handled effectively.

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💬 Task 2: Text Sentiment Analysis

Sentiment Analysis Model

📌 Objective

Develop a sentiment analysis model to classify text as positive or negative. This involves preprocessing text, feature extraction, model training, and evaluation using metrics like precision, recall, and F1-score.

🛠️ Features

• Text preprocessing (tokenization, stopword removal, lemmatization)
• Feature extraction using TF-IDF or word embeddings
• Model training using Logistic Regression or Naive Bayes
• Evaluation metrics (precision, recall, F1-score)

📂 Installation

# Clone the repository
git clone https://github.com/your-username/sentiment-analysis.git
cd sentiment-analysis

# Create a virtual environment
python -m venv venv
source venv/bin/activate  # On Windows use `venv\Scripts\activate`

# Install dependencies
pip install -r requirements.txt

🚀 Usage

# Run the script
python sentiment_analysis.py --input "This movie was amazing!"

🔍 Example Output

Input: "This movie was amazing!"
Predicted Sentiment: Positive
Accuracy: 89.5%

🏗️ Model Training

python train_model.py --dataset imdb_reviews.csv

📊 Evaluation

python evaluate_model.py

🔍 Task 3: Fraud Detection System

Fraud Detection System

📌 Project Overview

This project builds a fraud detection system using machine learning to classify credit card transactions as fraudulent or legitimate. It uses the Credit Card Fraud Dataset, applies data preprocessing, handles class imbalance with SMOTE, and trains a Random Forest model to detect fraud.

🚀 Features

• Preprocessing: Data cleaning, normalization, and class balancing.
• Machine Learning Model: Uses Random Forest for classification.
• Evaluation Metrics: Measures precision, recall, and F1-score.
• Interactive Testing: Allows users to input transaction data for real-time fraud detection.

📂 Dataset

The dataset used is creditcard.csv, which contains anonymized transaction data with features like Time, Amount, and V1-V28.

🔧 Installation & Setup

1. Clone this repository:

   git clone https://github.com/your-username/fraud-detection.git
   cd fraud-detection

2. Install dependencies:

   pip install pandas numpy scikit-learn imbalanced-learn matplotlib seaborn

3. Run the fraud detection script:

   python fraud_detection.py

📊 Model Training & Evaluation

The model is trained on processed data, and evaluated using:

• Confusion Matrix
• Classification Report (Precision, Recall, F1-score)

🛠 Usage

Running the System

To manually test a transaction, use:

python fraud_detection.py

Enter transaction details as prompted.

Example Automated Test

Modify the script to test with a predefined transaction:

example_transaction = X_test[0].reshape(1, -1)
prediction = model.predict(example_transaction)
print("Prediction:", "Fraudulent" if prediction[0] == 1 else "Legitimate")

🤖 Future Enhancements

• Implementing deep learning models.
• Deploying the model as a REST API.
• Creating a web-based dashboard for monitoring.

🏡 Task 4: Predicting House Prices (California Housing Dataset)

🚀 Features of This Script

✅ Custom Linear Regression & Random Forest Implementations

✅ Preprocessing: Normalization & Categorical Encoding

✅ Performance Metrics: RMSE & R² Score

✅ Graphical Comparison of Model Performance

📥 Dataset Information

The dataset is from the California Housing Dataset, containing features like:

• longitude, latitude - Location coordinates
• housing_median_age - Median age of houses
• total_rooms, total_bedrooms - Number of rooms and bedrooms
• median_income - Median income of residents
• ocean_proximity - Categorical feature (distance from ocean)
• median_house_value - Target variable (House Price)

📌 Source: California Housing Dataset

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📊 Model Implementations

This project includes custom implementations of three regression models:

Linear Regression (From Scratch)

Random Forest (From Scratch)

XGBoost (From Scratch)

🛠️ Contribution Guide

We welcome contributions! 🎉 To contribute:

Fork the repository 🍴

Create a new branch (feature-branch)

Commit your changes (git commit -m "Add feature XYZ")

Push to GitHub (git push origin feature-branch)

Create a Pull Request 📩

Happy Coding! 🎯🚀