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 contact me to learn more about my experience working with these projects.

Portuguese Bank Marketing Analysis

Skills used: Python, Pandas, SKlearn, Matplotlib

Project Objective: In this project I worked with real world data which is related with direct marketing campaigns (phone calls) of a Portuguese banking institution. The classification goal is to predict if the client will subscribe a term deposit.

Quantifiable result: We could Classify the type of tumor resulting in 98% accuracy using Logistic Regression algorithm.

Used logistic regression to classify whether term deposit is subscribed or not.
Classified subscription depending on given independent features.
Explored a machine learning approach to marketing analysis.

Orthopedic Patients Prediction

Skills used: Python, Pandas, SKlearn, Matplotlib

Project Objective: In this project we predicted normal and abnormal status of patient based on orthopedic parameters using KNN and NB algorithm.

Quantifiable result: We could train the data with KNN algo to attain a accuracy of 91%.

Used KNN and NB to predict if patient has disease or not.
Compared Best Algorithm.
Explored machine learning approach to Medical Data.

Talking Data Fraud Detection with Ensemble Technique

Skills used: Python, Pandas, SKlearn, Matplotlib

Project Objective: In this problem, we will use the features associated with clicks, such as IP address, operating system, device type, time of click etc. to predict the probability of a click being fraud.

Quantifiable result: We could predict whether a given click resulted in a download or not. The Accuracy of Boosting technique is 92% And accuracy of Baaging is 99% .

Used XGBoost Classifier algo to classify the people who downloaded the app after watching the advertisement and identify the click fraud
Compared predictive performance by fitting Bagging Classifier model to the data
Selected best model based on train and test performance
Explored Ensemble Technique.

Identifying given picture is a Cat or a Dog

Skills used: Python, Keras, Tensorflow

Project Objective: Prediction of whether a given image is a Cat or a Dog using Convolutional Neural Networks which may be further implemented as a feature in a bigger project.

Quantifiable result: We could train the Convolutional Neural Network to attain a accuracy of 85% using 25 epochs.

Added multiple convolution and pooling layers
Training model on basis of given data
Fitting the CNN to see if the provided image is dog or cat
Data Source: https://drive.google.com/drive/folders/15SG-chdqEwcrNAY39RTZJjvl-UwiZo_e?usp=sharing