Data Science Project Portfolio

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Data Science Project

Fashion MNIST dataset training using PyTorch

This project is a part of the Bertelsmann Tech Scholarship AI Track Nanodegree Program from Udacity. In this project, we are going to use Fashion MNIST data sets, which is contained a set of 28X28 greyscale images of clothes. Our goal is building a neural network using Pytorch and then training the network to predict clothes. This trained network will return a probability for 10 classes of clothes shown in images.

Link to the Medium article

House Price Prediction:Kaggle Competition

In this Kaggle competition, I have been predicting the house price with a lot of explanatory variables describing (almost) every aspect of residential homes in Ames, Iowa. Python programming and regression techniques like the random forest were used in this competition. Please follow the link to see Kaggle’s notebook.

House Price Prediction:Kaggle Competition

Titanic Machine Learning Competition

The competition is: use machine learning to create a model that predicts which passengers survived on the Titanic shipwreck.I used predictive modeling to find out the best algorithm to predict survived people. Logistic Regression, Linear Support Vector Machine, Radical Support Vector Machine, Decison Tree, K-Nearest Neighbours(KNN), Gaussian Naive Bayes, Random Forest algorithm has been used to find the best model. Please visit the Kaggle notebook for more information. Titanic Machine Learning Competition

Data Analysis

Investigating TMDB Movie Datasets

In this project, I have been used the TMDB movies dataset which is collected between 1960 to 2015 with the information of title, budget, revenue, cast, director, genres, release date, release year, runtime, etc The primary goal of the project is making the exploratory data analysis using numpy, pandas, seaborn and matplotlib library.

Investigating TMDB Movie Datasets

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