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BENGALURU PRICE PREDICTION
The Bengaluru house price dataset from Kaggle was used for this project where a machine learning model was built.
The heart of the solution was in the data-preprocessing as this dataset had a lot of inconsistencies.
At the end, XGBoost was chosen as the final model and a price prediction app was made using Flask, HTML, CSS and JavaScript.
View the product video -->
MyHOME.mp4House_price_report.pdf<-- View the report
View the dashboard -->
CRAB AGE PREDICTION
A comprehensive analysis of the crab age prediction dataset sourced from Kaggle was done. Various models were developed and evaluated using Python, where Random Forest ultimately emerged as the most effective solution. Throughout the project, many challenges were encountered and successfully addressed. E.g. presence of outliers and multicollinearity were dealt with using appropriate methodologies. This journey not only yielded valuable insights but also significantly enriched the understanding of building machine learning models.
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CrabAge_report.pdfGENERAL HEALTH DASHBOARD
Cardiovascular data from Kaggle was used where General Health of an individual can be visualized using this interactive dashboard. The dashboard was made using Tableau.
To view the dashboard: Click Here
REGRESSION ANALYSIS - DIAMONDS
Explanatory data analysis was conducted, and a model was built using Multiple Linear Regression to determine which variables most affect diamond price, leading to successful prediction of the diamond price with a strong coefficient of determination of 0.97. Challenges were faced due to the presence of many outliers and multicollinearity, which were satisfactorily addressed through data preprocessing and the removal of a few variables.
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Diamonds_report.pdf
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