https://github.com/camilajaviera91/linear-regression-first-approach
This project implements a simple linear regression model to predict ice cream sales based on temperature. It includes user interaction for collecting data, building the regression model, making predictions, and visualizing results.
https://github.com/camilajaviera91/linear-regression-first-approach
fit linear-regression numpy-arrays python sklearn
Last synced: 7 months ago
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This project implements a simple linear regression model to predict ice cream sales based on temperature. It includes user interaction for collecting data, building the regression model, making predictions, and visualizing results.
- Host: GitHub
- URL: https://github.com/camilajaviera91/linear-regression-first-approach
- Owner: CamilaJaviera91
- Created: 2024-12-02T15:37:13.000Z (10 months ago)
- Default Branch: main
- Last Pushed: 2025-01-03T14:19:59.000Z (9 months ago)
- Last Synced: 2025-01-30T15:19:29.055Z (8 months ago)
- Topics: fit, linear-regression, numpy-arrays, python, sklearn
- Language: Python
- Homepage:
- Size: 15.6 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# Ice Cream Sales Prediction Using Linear Regression
This project implements a simple linear regression model to predict ice cream sales based on temperature. It includes user interaction for collecting data, building the regression model, making predictions, and visualizing results.
---
## Features
- **Data Collection**: Interactive script for inputting temperature and sales data.
- **Linear Regression Model**: Uses `scikit-learn` to train a regression model on the collected data.
- **Prediction**: Allows users to input a temperature and predicts corresponding ice cream sales.
- **Visualization**: Generates a scatter plot of the data points and the regression line.
---
## Files Description
### `collect_data.py`
This script collects temperature and sales data from the user interactively. It ensures the data is consistent and converts it into NumPy arrays for further processing.
#### Key Functions:
- **`collect_data()`**:
- Prompts the user to input temperature and sales values.
- Ensures both lists have the same length.
- Converts the data into NumPy arrays for model training.
### `linear_regression.py`
This script handles the creation and training of the linear regression model. It:
- Collects data using the `collect_data` module.
- Trains a regression model using the collected data.
- Makes predictions based on user input.
- Visualizes the results with a scatter plot and regression line.
#### Steps:
1. **Data Collection**: Uses `collect_data()` from `collect_data.py`.
2. **Data Sorting**: Sorts data for better visualization.
3. **Model Training**: Trains a linear regression model on the collected data.
4. **Prediction**: Prompts the user for a temperature value to predict sales.
5. **Visualization**: Plots actual data and the regression line.
### `prediction.py`
Contains a utility function to get a valid temperature input for predictions.
#### Key Functions:
- **`get_prediction()`**:
- Prompts the user for a temperature value.
- Ensures the input is a valid integer.
---
## Installation
1. Clone the repository:
```bash
git clone https://github.com/yourusername/ice-cream-sales-prediction.git
cd ice-cream-sales-prediction
```
2. Install dependencies:
```bash
pip install -r requirements.txt
```
---
## Usage
1. Run the `linear_regression.py` script:
```bash
python linear_regression.py
```
2. Follow the prompts to:
- Input temperature and sales data.
- Input a temperature for sales prediction.
3. View the scatter plot and regression line for the data.
---
## Example
### Input Data:
```text
Temperature: 25, 30, 35
Sales: 200, 300, 400
```
### Prediction:
```text
Enter a temperature to predict sales: 28
Prediction: Approximately 240 ice creams will be sold.
```
### Visualization:
A plot showing the relationship between temperature and sales, along with the regression line.
---
## Requirements
- Python 3.7+
- NumPy
- Matplotlib
- scikit-learn