Data Science

Case Studies

Case Studies in Data Science

Case study 01: Sales Forecast Prediction - Python

Sales Forecast Prediction

Developed a machine learning–based sales forecasting system using historical sales data to identify time-based patterns and predict future sales. The project includes data preprocessing, sales trend visualization, lag-feature engineering, chronological train/test splitting, and XGBoost regression for forecasting, with model performance evaluated using RMSE.

Technologies: Python • Pandas • NumPy • Matplotlib • Seaborn • Scikit-learn • XGBoost • Machine Learning • Time-Series Forecasting

Case study 02: Customer Churn Analysis Prediction - Python

Customer Churn Analysis & Prediction

Developed a machine learning classification project to analyze customer behavior and predict potential churn using the Telco Customer Churn dataset. The project covers data cleaning, categorical encoding, feature scaling, exploratory data analysis, Random Forest classification, and model evaluation using accuracy and a confusion matrix to identify customers at risk of leaving.

Technologies: Python • Pandas • NumPy • Matplotlib • Seaborn • Scikit-learn • Random Forest • Machine Learning • Classification

Case study 03: Inventory Demand Forecasting using Machine Learning - Python

Inventory Demand Forecasting Using Machine Learning

Developed a machine learning–based inventory demand forecasting system to predict product demand across multiple stores. The project involved data preprocessing, feature engineering, exploratory data analysis, trend analysis, outlier handling, feature scaling, and comparison of multiple regression models, with XGBoost achieving the lowest validation error in the referenced implementation.

Technologies: Python • Pandas • NumPy • Matplotlib • Seaborn • Scikit-learn • XGBoost • Machine Learning • Feature Engineering • Data Visualization

Case study 04: Customer Segmentation using Unsupervised Machine Learning in Python

Customer Segmentation Using Unsupervised Machine Learning

Developed an unsupervised machine learning solution to identify meaningful customer groups based on customer characteristics and purchasing behavior. The project covers data cleaning, categorical encoding, feature scaling, t-SNE dimensionality reduction, K-Means clustering, the Elbow Method for selecting the number of clusters, and visualization of customer segments to support targeted marketing and data-driven business decisions.

Technologies: Python • Pandas • NumPy • Matplotlib • Seaborn • Scikit-learn • K-Means Clustering • t-SNE • Unsupervised Machine Learning • Feature Engineering • Data Visualization

Case study 05: Analyzing Selling Price of used Cars using Python

Used Car Selling Price Analysis

Analyzed a used-car dataset to uncover the key factors influencing vehicle selling prices. The project covers data cleaning, feature normalization, categorical encoding, exploratory analysis, visualization, grouping, pivot tables, heatmaps, and ANOVA-based statistical analysis to identify relationships between vehicle characteristics and price.

Technologies: Python • Pandas • NumPy • Matplotlib • Seaborn • SciPy • Data Cleaning • Feature Normalization • Exploratory Data Analysis • Statistical Analysis • Data Visualization