Data Science Course in Delhi

Customer Churn Prediction

This project focuses on predicting customer churn for a subscription-based service, such as telecom or streaming platforms. Starting with Problem Definition and Dataset Selection, participants define key objectives and choose a dataset containing customer information, subscription details, and historical churn data. The next phase, Exploratory Data Analysis (EDA) and Visualization, involves analyzing patterns, identifying factors influencing churn, and visualizing trends using tools like Matplotlib or Tableau. In Building and Validating Predictive Models, participants apply machine learning algorithms like Logistic Regression, Random Forest, or Gradient Boosting to classify customers as likely to churn or retain. After model validation with metrics like precision, recall, and F1 score, participants proceed to Presenting Insights and Final Deliverables by creating a business report detailing actionable recommendations to reduce churn.

House Price Prediction

In this project, participants predict house prices based on features like location, size, and amenities. The Problem Definition and Dataset Selection phase focuses on understanding key variables and using datasets like Kaggle’s House Price dataset. Through EDA and Visualization, participants analyze correlations between variables and visualize trends such as price variations by location. Using algorithms like Linear Regression or Decision Trees, participants build predictive models, evaluating performance with RMSE (Root Mean Square Error). Finally, Presenting Insights and Final Deliverables includes a detailed report with visualizations and recommendations for buyers or real estate companies.

Retail Sales Forecasting

Participants predict future sales for a retail store, using historical data. Problem Definition and Dataset Selection involves understanding sales trends and selecting datasets with transaction histories. During EDA and Visualization, participants examine seasonality, product trends, and customer behavior. In Building Predictive Models, techniques like ARIMA or LSTM are applied for time series forecasting. Final Deliverables include actionable insights to optimize inventory management and marketing strategies.

Sentiment Analysis on Product Reviews

This project analyzes customer sentiment from online reviews. Dataset Selection includes gathering reviews from platforms like Amazon or Yelp. EDA explores word frequencies and common themes using word clouds. Participants use Natural Language Processing (NLP) techniques and models like Naive Bayes or BERT for Building and Validating Models. Insights and Deliverables provide visualizations and sentiment-based recommendations for product improvements.