IPL Data Analysis Dashboard

This project performs end-to-end exploratory data analysis (EDA) on IPL auction data and builds an interactive dashboard for business insights.

Project Overview

The notebook cleans and transforms raw IPL auction data, engineers useful features, detects outliers, generates an automated profiling report, and publishes a multi-panel interactive dashboard.

Objectives

• Clean and standardize IPL auction dataset fields.
• Handle missing values using domain-aware logic.
• Create derived features for richer analysis.
• Detect high-value outlier players using IQR.
• Generate interactive visual analytics outputs.

Workflow

1. Load raw data from ipl_dataset.csv.
2. Clean column names and remove unnecessary fields.
3. Handle missing values in cost/team columns.
4. Engineer features such as Is_Retained, Price_Category, and Value_Ratio.
5. Detect outliers and flag premium players (Is_High_Value).
6. Export cleaned dataset to ipl_cleaned.csv.
7. Create profiling report and dashboard HTML outputs.

Key Outputs

• ipl_cleaned.csv: Cleaned, feature-enriched analysis-ready dataset.
• IPL_EDA_Report.html: Automated EDA report with statistics and quality checks.
• IPL_2023_Dashboard.html: Interactive dashboard with six analytical panels.
• final_prop.ipynb: Full notebook containing code, analysis, and documentation cells.

Dashboard Insights Included

• Team-wise total spending.
• Player type distribution.
• Top 10 expensive players.
• Price category breakdown.
• Retained vs auctioned player comparison.
• Cost distribution by player type.

Learning Outcomes

By completing this project, you will learn how to:

• Structure a practical data analysis pipeline from raw data to presentation.
• Apply robust missing-value handling and feature engineering techniques.
• Use IQR-based statistical methods for outlier detection.
• Build clear, interactive Plotly dashboards for stakeholder reporting.
• Create reproducible analytical deliverables in notebook and HTML formats.

Tech Stack

• Python
• Pandas, NumPy
• Matplotlib, Seaborn
• Plotly
• SciPy
• ydata-profiling

Run the Project

1. Install dependencies from requirements.txt.
2. Open and run final_prop.ipynb cells in order.
3. Review generated outputs in the project folder.