Data Science Portfolio

Here are some of my best Data Science Projects. I have explored various machine-learning algorithms for different datasets. Feel free to contanct me to learn more about my experience working with these projects.

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Predicting a Patient Heart Condition

• Skills Used: Python,sklearn,logistic Regression,pandas,numpy, pickle

• Project Objective: Predicting Heart Disease. Analyze the data set and identify most relevant heart disease related risk factors as well as predict the overall risk.

• Quantifiable Result: Used various logisitic models to find the appropreite model to get more accurate scores But By using Random Forest we got accuracy 94%

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Predicting House Pricing Based

• Skills used: Python, Sklearn, Linear Regression, Pandas,Numpy, Matplotlib, Seaborn

• Project Objective:In this Project We have to explore the Data and Predict the House Price.

• Quantifiable Result: We can Predict the House Price with 89% Accuracy

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Examining the effect of environmental factors and Weather on Bike rentals

• Skills used: sklearn,pandas,numpy,matplotlib,seaborn,Linear Regression

• Project Objective: By predicting the bike rental demand in advance from weather forecast, Bike Rental Company position the bike according to customers demands resulting in increase in bike utilization.

• Quantifiable Result: We can Predict the Bike Rental Demand Price with 88.39% Accuracy

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Identifying Customers Likely to Subscribe for Term Deposit

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• Skills Used: Python,Numpy, Pandas, Matplotlib, Seaborn, Smote, Logisitic Regression,

• Project Objective: The Main Objective is to predict if the client will subscribe a term deposit (variable y).

• Quantifiable Result: We can Predict the Customer’s Response with 91.25% accuracy.

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Identifying symptoms of orthopedic patients as normal or abnormal

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• Skills Used: Pandas, Numpy, sklearn, Matplotlib, Seaborn, Logistic Regression, K-Nearest Neighbor

• Project Objective: Used the K Nearest Neighbours algorithm to classify a patient’s condition as normal or abnormal based on various orthopedic parameters

• Quantifiable Result: We can Identify Symptoms of orthopedic patients as Normal or abnormal with 85.95% accuracy