🚀 𝐄𝐧𝐝-𝐭𝐨-𝐄𝐧𝐝 𝐃𝐚𝐭𝐚 𝐏𝐫𝐨𝐜𝐞𝐬𝐬𝐢𝐧𝐠 𝐏𝐢𝐩𝐞𝐥𝐢𝐧𝐞 𝐟𝐨𝐫 𝐂𝐨𝐮𝐫𝐬𝐞 𝐂𝐨𝐦𝐩𝐥𝐞𝐭𝐢𝐨𝐧 𝐏𝐫𝐞𝐝𝐢𝐜𝐭𝐢𝐨𝐧

🔍 From Raw Data → Insights → ML Models → Final Best Regression Model

🏗️ Analytical Platform

Designed a scalable environment for end-to-end data analysis using Python, Pandas, NumPy, Seaborn & Scikit-Learn.

📥 Data Ingestion

Loaded the dataset seamlessly and performed initial schema checks and validations.

🧹 Data Cleaning

Handled missing values, corrected datatype issues, removed outliers, and standardized the dataset for analysis.

🔍 Univariate Analysis

Studied individual features using histograms, countplots & descriptive statistics.

🔗 Bivariate Analysis

Compared relationships between independent features and the target variable using boxplots, scatterplots & heatmaps.

🔀 Multivariate Analysis

Explored complex interactions using correlation matrices, pairplots & multivariate heatmaps.

🧬 Feature Engineering

Created new useful variables, encoding categorical features & scaling numerical data for model efficiency.

🤖 Machine Learning

Built multiple Regression Models including:

✔ Linear Regression

✔ Random Forest Regression

✔ Gradient Boosting

✔ Decision Tree Regression

📊 Implement & Evaluate Models

Evaluated models based on MAE, MSE, RMSE, and R² Score.

📈 Visualize Regression Models Performance

Compared error metrics across models using bar plots, residual plots & prediction vs actual plots.

🏆 Select & Visualize Best Regression Model

Identified the best-performing model and visualized its predictions & performance metrics.