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Welcome to my Data Science Projects repository! This repository includes a collection of projects I worked on during my internship, focusing on various data science applications. Below is a brief description of each project.
- Description: This project involves building a predictive model to identify customers who are likely to churn (leave the bank). The model utilizes various features such as customer demographics, account information, and transaction history.
- Technologies Used: Python, Pandas, Scikit-learn, Matplotlib, Seaborn
- Key Tasks:
- Data cleaning and preprocessing
- Exploratory data analysis
- Feature engineering
- Model building and evaluation (Logistic Regression, Random Forest, etc.)
- Model tuning and optimization
- Description: This project focuses on creating a recommendation system to suggest movies to users based on their viewing history and preferences. The system employs collaborative filtering techniques to enhance user experience.
- Technologies Used: Python, Pandas, Numpy, Scikit-learn, Surprise Library, Matplotlib
- Key Tasks:
- Data cleaning and preprocessing
- Exploratory data analysis
- Implementing collaborative filtering (User-based and Item-based)
- Model evaluation using metrics like RMSE
- Enhancing recommendation accuracy
- Description: This project involves analyzing financial market news to determine the sentiment (positive, negative, neutral) and its potential impact on market trends. The analysis aims to provide insights that can assist in financial decision-making.
- Technologies Used: Python, Pandas, NLTK, Scikit-learn, VADER Sentiment Analysis, Matplotlib
- Key Tasks:
- Data collection and preprocessing
- Text cleaning and normalization
- Sentiment analysis using VADER
- Model building and evaluation (Naive Bayes, SVM, etc.)
- Visualization of sentiment trends