README.md

InternSavy

This repository contains the data science notebooks and datasets for my internship.

Project Structure

The project is organized into the following main folders:

• Task_1: Contains Jupyter notebooks with the Admission Prediction analyses, ML models and Dataset in CSV file for Task 1.

• Task_2: Contains Jupyter notebooks with the Clustering Models' analyses, Clustering models and Dataset in CSV file for Task 2.

• Task_3: Contains Jupyter notebooks with the Customer Segmentation analysis, ML models and Dataset in CSV file for Task 3.

Notebooks

1. Notebook 1 - Admission Prediction using Linear Regression & Ridge Regression:

   • Filename: t1.ipynb
   • Description: This notebook implements linear regression and ridge regression models to predict admission outcomes based on various features. It covers data preprocessing, model training, and evaluation.

2. Notebook 2 - Clustering Analysis of different algorithms on customer dataset:

   • Filename: t2.ipynb
   • Description: This notebook explores clustering algorithms such as k-means, agglomerative hierarchy, spectral clustering, and mean shift on a customer dataset. It includes data preprocessing, model implementation, and visualization of clustering results.

3. Notebook 3 - Customer Segmentation Analysis:

   • Filename: t3.ipynb
   • Description: This notebook focuses on customer segmentation analysis. It includes exploratory data analysis (EDA), feature engineering, and the application of clustering techniques to identify distinct customer segments.

Datasets

• Dataset 1 - Admission Predict Ver1.1:

  • Filename: Admission_Predict_Ver1.1.csv
  • Description: Dataset containing features related to admission predictions.

• Dataset 2 - Mall Customers:

  • Filename: Mall_Customers.csv
  • Description: Dataset used for clustering analysis to understand customer behavior in a mall.

• Dataset 3 - Segmentation Data:

  • Filename: segmentation_data.csv
  • Description: Dataset used for customer segmentation analysis to identify distinct customer segments.

Tech Stack

This project is developed using the following technologies:

• Python 3.x: The primary programming language for data analysis and machine learning.

• Jupyter Notebook: Utilized for interactive and exploratory coding.

• Libraries: Various Python libraries including NumPy, Pandas, Matplotlib, Seaborn, and Scikit-Learn for data manipulation, visualization, and machine learning tasks.