๐ ๐๐ง๐-๐ญ๐จ-๐๐ง๐ ๐๐๐ญ๐ ๐๐ซ๐จ๐๐๐ฌ๐ฌ๐ข๐ง๐ ๐๐ข๐ฉ๐๐ฅ๐ข๐ง๐ ๐๐จ๐ซ ๐๐จ๐ฎ๐ซ๐ฌ๐ ๐๐จ๐ฆ๐ฉ๐ฅ๐๐ญ๐ข๐จ๐ง ๐๐ซ๐๐๐ข๐๐ญ๐ข๐จ๐ง
๐ From Raw Data โ Insights โ ML Models โ Final Best Regression Model
๐๏ธ Analytical Platform
Designed a scalable environment for end-to-end data analysis using Python, Pandas, NumPy, Seaborn & Scikit-Learn.
๐ฅ Data Ingestion
Loaded the dataset seamlessly and performed initial schema checks and validations.
๐งน Data Cleaning
Handled missing values, corrected datatype issues, removed outliers, and standardized the dataset for analysis.
๐ Univariate Analysis
Studied individual features using histograms, countplots & descriptive statistics.
๐ Bivariate Analysis
Compared relationships between independent features and the target variable using boxplots, scatterplots & heatmaps.
๐ Multivariate Analysis
Explored complex interactions using correlation matrices, pairplots & multivariate heatmaps.
๐งฌ Feature Engineering
Created new useful variables, encoding categorical features & scaling numerical data for model efficiency.
๐ค Machine Learning
Built multiple Regression Models including:
โ Linear Regression
โ Random Forest Regression
โ Gradient Boosting
โ Decision Tree Regression
๐ Implement & Evaluate Models
Evaluated models based on MAE, MSE, RMSE, and Rยฒ Score.
๐ Visualize Regression Models Performance
Compared error metrics across models using bar plots, residual plots & prediction vs actual plots.
๐ Select & Visualize Best Regression Model
Identified the best-performing model and visualized its predictions & performance metrics.