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This Streamlit app provides a comprehensive toolkit for data exploration, visualization, cleaning, and basic machine learning. It is designed to facilitate various stages of a data science project, making it an essential tool for data scientists and analysts.
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NaimuL0/Data-Science-Project-with-Streamlit
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This Streamlit app provides a comprehensive toolkit for data exploration, visualization, cleaning, and basic machine learning. It is designed to facilitate various stages of a data science project, making it an essential tool for data scientists and analysts.
#Features
Exploratory Data Analysis (EDA)Upload Datasets: Supports CSV, TXT, and XLSX files.View Data: Display the first few rows of the dataset.Data Summary: Show the shape and descriptive statistics of the dataset.Column Information: List all columns and allow the selection of specific columns for detailed analysis.Correlation Matrix: Visualize correlations between features using heatmaps.Scatter Plot: Create scatter plots for any two selected features.
Data VisualizationPlot Types: Generate various plots including area, bar, line, histogram, box, KDE, pair plots, and scatter matrix.Interactive Plots: Utilize Plotly to create interactive scatter matrix plots.
Data CleaningHandle Missing Values: Fill missing values with column means.Drop Columns: Remove unwanted columns from the dataset.
Machine LearningModel Training: Train a Random Forest classifier on selected features.Model Evaluation: Display classification reports and confusion matrices.
Download Processed DataDownload CSV: Allow users to download the processed DataFrame as a CSV file.
**How to Use
Upload your dataset: Choose from CSV, TXT, or XLSX file formats.Select an activity: Choose from EDA, Plots, Data Cleaning, Machine Learning, or Download.Perform analysis and visualization: Use the various tools and options provided to explore and analyze your data.Download the processed data: Save your cleaned and augmented data for further use.
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This Streamlit app provides a comprehensive toolkit for data exploration, visualization, cleaning, and basic machine learning. It is designed to facilitate various stages of a data science project, making it an essential tool for data scientists and analysts.
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