Atharva Kulkarni Portfolio
Data Science Project Portfolio (The project names are links.)
Project 1 :- Amazon Review Scrapping and Emotion Mining
Amazon product reviews scrapping and emotion mining. Reviews are an important part of buying products. When buying something the consumer looks at the ratings and reviews of the product. This project focuses on the reviews of a product on Amazon. GODS Ghost 22 Litre Anti-Theft 15.6 Inch Laptop Backpack (Carbon Fibre Colour) By using python libraries we rate the emotions of the reviews in positive and negative categories.
Technologies used - Python, Jupyter Notebook, Pandas, Numpy, Matplotlib, URL.Lib, Beautiful Soup.
Project 2 :- Machine Learning Real Estate Price Prediction
A real estate price prediction model built in Python with Jupyter Notebook. This project focuses on building a real estate price prediction model using sklearn and linear regression using bangalore home prices dataset from kaggle.com. The model building covers data science concepts like data loading, data cleaning, outlier detection and removal, feature engineering, dimensionality reduction, gridsearchcv for hyperparameter tuning, k fold cross validation.
Technology used;
- Python
- Numpy and Pandas for Data Cleaning
- Matplotlib for Data visualisation
- Sklearn for model building
- Jupyter notebook as IDE
Project 3 :- IPL EDA Project
An exploratory data analysis project for the Indian Premier League cricket tournament with Python. The Indian Premier League (IPL) is a very popular professional men’s Twenty20 cricket league, during the time of this tournament millions of Indians are glued to their tv screens watching and supporting their favourite teams which represent different cities in India. This is a small explarotary data analysis project which focuses on match data and ball by ball data of matches between the year 2008 and 2020.
Technologies used: Python, Pandas, Numpy, Matplotlib, Seaborn, Github.
Project 4 :- Elon Musk Twitter Emotion Mining Project
emotion mining code on tweets of Elon Musk.
Emotion Mining Emotion mining is the science of detecting, analyzing, and evaluating humans’ feelings towards different events, issues, services, or any other interest. One of its specific directions is text emotion mining, that refers to analyzing people’s emotions based on observations of their writings.
This small project analyses the tweets of Elon Musk combined together in a dataset using technologies like re, matplotlib, pandas and textblob.
Project 5 :- Data Science Concepts
Set of codes on different data science concepts in Python. This project repository focuses on some important data science and AI concepts with a demonstration of how they work, the concepts used in this repository are:
- Association Rules
- Clustering
- Decision Trees
- Forecasting
- Hypothesis Testing
- K-Nearest Neighbours
- Logistic Regression
- Multiple Linear Regression
- Naive Bayes
- Neural Networks
- Principal Component Analysis
- Random Forests
- Recommendation System
- Simple Linear Regression
- Support Vector Machines
Technologies used Python, Jupyter Notebook, Matplotlib, Seaborn, Pandas, Numpy, Scikit-Learn, Keras.
Project 6 - Covid-19 Analysis in R
Analysis of Covid-19 with R.
Coronavirus disease (COVID-19) is an infectious disease caused by the SARS-CoV-2 virus. Most people infected with the virus will experience mild to moderate respiratory illness and recover without requiring special treatment. However, some will become seriously ill and require medical attention. Older people and those with underlying medical conditions like cardiovascular disease, diabetes, chronic respiratory disease, or cancer are more likely to develop serious illness. Anyone can get sick with COVID-19 and become seriously ill or die at any age.
This project compares the effect of Covid-19 on the basis of age and Gender with the help of Hmisc library and the t-test in R studio.
Technologies used - R, R Studio, Hmisc Library.
