Data Science Internship Projects

Overview

This repository contains four comprehensive data analysis projects completed during my data science internship, focusing on different aspects of data analysis, machine learning, and visualization.

Projects

1. Titanic Survival Analysis

• Exploratory data analysis of Titanic passenger data
• Analysis of survival patterns based on demographics
• Visualization of key survival factors
• Statistical analysis and correlation studies

2. Bank Marketing Analysis

• Decision tree classification for marketing campaign success prediction
• Feature importance analysis
• Model evaluation and performance metrics
• Tree visualization and interpretation

3. Twitter Sentiment Analysis

• Analysis of sentiment distribution across tweets
• Entity-based sentiment classification
• Text processing and visualization
• WordCloud generation for common terms

4. Traffic Accident Analysis

• Temporal analysis of accident patterns
• Weather impact assessment
• Severity analysis based on road conditions
• Day-wise and hourly accident distribution

Technologies Used

• Python 3
• Libraries:
  • pandas: Data manipulation and analysis
  • scikit-learn: Machine learning implementations
  • matplotlib & seaborn: Data visualization
  • wordcloud: Text visualization
  • numpy: Numerical computations

Setup Instructions

1. Install required packages:

pip install pandas numpy matplotlib seaborn scikit-learn wordcloud

2. Dataset requirements:

• Titanic.csv
• Bank_Marketing.csv
• twitter_sentiment_analysis.csv
• traffic_accidents.csv

Key Findings

Titanic Analysis

• Gender and passenger class significantly influenced survival rates
• Age distribution varied across passenger classes

Bank Marketing

• Decision tree achieved significant prediction accuracy
• Identified key features influencing campaign success

Sentiment Analysis

• Distribution of positive, negative, and neutral sentiments
• Entity-specific sentiment patterns

Traffic Analysis

• Peak accident hours identified
• Weather condition impact on accident frequency
• Weekly accident patterns analyzed

Project Status

All projects completed successfully with detailed documentation and visualizations

Future Improvements

• Implementation of advanced ML models
• Interactive dashboard development
• Deep learning applications
• Cross-project pattern analysis

Author

Aman Kamble Data Science Intern