Data Cleaning & Analysis Assistant

This project automates the cleaning and analysis of messy CSV/Excel data. It identifies missing values, detects outliers, normalizes data, and generates insightful reports with visualizations.

Features

• Handles missing values (fills with mean/median or "Unknown" for categorical data)
• Detects outliers using the Interquartile Range (IQR) method
• Normalizes numerical data using Min-Max Scaling
• Generates statistical reports and visualizations (Matplotlib & Seaborn)
• Saves the cleaned dataset for further analysis

Tech Stack

• Python 
• Pandas (for data manipulation)
• Matplotlib & Seaborn (for data visualization)
• Jupyter Notebook (for interactive execution)

Installation

1. Clone the Repository:

   git clone https://github.com/kbhavneet/data-cleaning-project.git
   cd data-cleaning

2. Install Dependencies:

   pip install pandas matplotlib seaborn openpyxl numpy scikit-learn

3. Run Jupyter Notebook:

   jupyter notebook

   • Open data_cleaning_final.ipynb and execute the cells.

Usage

1. Place Your Dataset (.csv or .xlsx) inside the project folder as sample_data.csv.
2. Run the Jupyter Notebook to clean and process the data.
3. View the Insights:
   • Processed data is saved as cleaned_data.csv.
   • Visualization plots display trends and distributions.

Example Output

Raw Data (Before Cleaning)

Salary Distribution Visualization

Cleaned Data (After Processing)

❤️