This repository contains data science projects implemented using Streamlit, a web application framework for Python. Each project is designed to demonstrate different data science concepts and techniques.
This project displays the stock closing price and volume for Google (GOOGL) using the YFinance library.
- Install Streamlit and YFinance packages:
pip install streamlit pip install yfinance
- Run the streamlit app using the following command:
cd 1_simple_stock_price streamlit run app.py
This project counts the nucleotide composition of a query DNA sequence and visualizes the results using Altair.
- Run the streamlit app using the following command:
cd 2_simple_bioinformatics_dna streamlit run app.py
This project performs exploratory data analysis (EDA) on NBA player statistics. It allows users to filter player stats by year, team, and position, and visualize the data using various charts and heatmaps.
- Run the Streamlit app using the following command:
cd 3_eda_basketball streamlit run app.py
This project performs exploratory data analysis (EDA) on Football player statistics. It allows users to filter player stats by year, team, and position, and visualize the data using various charts and heatmaps.
- Run the Streamlit app using the following command:
cd 4_eda_football streamlit run app.py
This project performs exploratory data analysis (EDA) on S&P 500 stock prices. It retrieves the list of S&P 500 companies, allows users to filter companies by sector, and visualizes the stock closing prices for the selected companies.
- Run the Streamlit app using the following command:
cd 5_eda_sp500_stock streamlit run app.py
This project demonstrates a classification model using the famous Iris dataset. The Iris dataset contains measurements of sepal length, sepal width, petal length, and petal width for three different species of iris flowers: setosa, versicolor, and virginica.
The application is built using Streamlit and allows users to input measurements for an iris flower. The model then predicts the species of the iris flower based on the input measurements and displays the prediction along with the prediction probabilities.
- Run the Streamlit app using the following command:
cd 7_classification_iris streamlit run app.py
This project demonstrates a classification model using the Palmer Penguins dataset. The dataset contains measurements for three different species of penguins: Adelie, Chinstrap, and Gentoo. The features include bill length, bill depth, flipper length, body mass, sex, and island.
The application is built using Streamlit and allows users to input measurements for a penguin. The model then predicts the species of the penguin based on the input measurements and displays the prediction along with the prediction probabilities.
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For the "penguins_size.csv" datasets use the "data_cleaning.ipynb" jupyter notebook and run all the cells.
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Now, for creating the Penguin Classification Model use the following command on "model_building.py" file:
python model_building.py
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Run the Streamlit app using the following command:
cd 8_classification_penguis streamlit run app.py
This project demonstrates a regression model using the Boston Housing dataset. The dataset contains various features of houses in Boston, such as crime rate, average number of rooms, and distance to employment centers, among others. The target variable is the median value of owner-occupied homes (MEDV).
The application is built using Streamlit and allows users to input features for a house. The model then predicts the median value of the house based on the input features and displays the prediction along with feature importance.
- Run the Streamlit app using the following command:
cd 9_regression_boston_housing streamlit run app.py
This project demonstrates a clustering model for customer segmentation. The dataset contains various features related to customer behavior, such as annual income, spending score, and age. The goal is to segment customers into different groups based on their behavior.
The application is built using Streamlit and allows users to visualize the clusters and understand the characteristics of each segment.
- Run the Streamlit app using the following command:
cd 10_clustering_customer_segmentation streamlit run app.py
This project presents a dashboard for visualizing the population trends in the United States from 2010 to 2019. The dataset includes population data for all states.
The application is built using Streamlit and provides various visualizations, including a choropleth map and a heatmap, to help users explore population changes over the years. Users can select a specific year to view detailed population data and migration trends for that year.
- Run the Streamlit app using the following command:
cd 11_us_population_dashboard streamlit run app.py