Real_time-data_Sales_prediction

Developed a real-time data analytics dashboard using Streamlit and a pre-trained machine learning model. The dashboard should simulate real-time data, make predictions using a trained regression and interactively display the data and predictions.

Features

• Simulated Sales Data: Randomly generated sales data for various grocery products.
• Regression Model: A linear regression model that predicts total sales based on the number of units sold and the price per unit.
• Real-Time Dashboard: Continuously updates with new sales data and predictions, displayed using Streamlit.
• Visualization: Graphs showing predicted sales trends over time.

Requirements

To run this project, you need Python and the following dependencies:

• streamlit: For building the interactive dashboard.
• pandas: For data manipulation.
• matplotlib: For visualizing data.
• joblib: For saving and loading the trained machine learning model.
• scikit-learn: For training the regression model.

Machine Learning Model

The regression model used in this project is a linear regression model that predicts the total sales based on the following features:

• Units Sold: The number of units of a product sold.
• Price per Unit: The price of each unit sold.

The model is trained using the scikit-learn library and saved using joblib for later use. The model predicts Total Sales based on the input features: Units Sold and Price per Unit.

Model Training

The model is trained on a simulated dataset of sales data where the target variable is Total Sales. The features used to train the model are:

• Units Sold
• Price per Unit

The training process involves fitting the model to the data and then using it to predict Total Sales. Once trained, the model is saved using joblib to allow for real-time predictions in the Streamlit dashboard.

Model Performance

After training the model, the following evaluation metrics were calculated:

• Mean Squared Error (MSE): [Insert the value after testing the model]
• R² Score: [Insert the R² score after testing the model]

A good R² score and a low MSE indicate that the model is performing well in predicting total sales.

Saving the Model

The trained model is saved as regression_model.pkl using joblib. This saved model is then loaded in the Streamlit app to make real-time predictions on new, simulated sales data.

Steps to Run the Project

Key Details:

1. Step 1 focuses on installing dependencies. It explains how to use pip install -r requirements.txt or manually install the libraries.
2. Step 2 covers how to run the Streamlit dashboard with streamlit run streamlit_dashboard.py and how to access it on localhost:8501.