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Nishad Khudabux's Projects

boston-house-price-prediction icon boston-house-price-prediction

Built a linear regression model to predict house prices in Boston. The final model is generalized and perfectly predicts prices with a 100% r-squared. Significant EDA and feature analysis were done to identify key features and make business recommendations moving forward.

data-science-in-golf-strokes-gained-vs-traditional-metrics icon data-science-in-golf-strokes-gained-vs-traditional-metrics

Unleashed the power of data science to analyze the performance of golfers from the PGA tour. Built ML models and compared Strokes Gained to traditional metrics, resulting in insightful findings and actionable recommendations for golfers at all levels. Showcased advanced data analysis, decision trees, and visualizations in this comprehensive project

diabetes-analysis icon diabetes-analysis

EDA (Exploratory Data Analysis) of the Pima Tribe to draw key insights into Diabetes from a controlled sample population.

fantasy-sports-clustering-analysis icon fantasy-sports-clustering-analysis

Performed clustering analysis on OnSports player data for the English Premier League. The clustering analysis successfully identified 4 unique player clusters and uncovered valuable business recommendations by identifying trends and patterns in the EDA, meeting the objective of determining player pricing next season.

loan-default-prediction icon loan-default-prediction

Built a classification model to predict clients who are likely to default on their loans. With the challenge of a limited dataset was able to build and tune a Random Forest Model maximized for a recall score of 80%. Significant EDA and feature analysis were done to identify key features and make business recommendations moving forward.

malaria-detection icon malaria-detection

Build a computer vision model to detect malaria from images of infected red blood cells. Model uses a CNN neural network to classify parasitized and uninfected cells with a 98.69% accuracy.

potential-customer-prediction icon potential-customer-prediction

A critical problem in EdTech is converting potential customers into paid customers. Performed EDA to identify the key factors driving the lead conversion process and built an ML model (using Decision Trees and Random Forest) that identifies which leads are more likely to convert.

street_number_classification icon street_number_classification

Build and train ANN and CNN model to process visual database of street view housing numbers, and recognize the digits in the image.

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