https://github.com/shadan100/sales-prediction-analysis
The aim is to build a predictive model and find out the sales of each product at a particular store. Using this model, BigMart will try to understand the properties of products and stores which play a key role in increasing sales.
https://github.com/shadan100/sales-prediction-analysis
artificial-intelligence data-analysis data-science django django-framework jupyter-notebook machine-learning matplotlib pandas predictive-modeling python sales-prediction
Last synced: 4 months ago
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The aim is to build a predictive model and find out the sales of each product at a particular store. Using this model, BigMart will try to understand the properties of products and stores which play a key role in increasing sales.
- Host: GitHub
- URL: https://github.com/shadan100/sales-prediction-analysis
- Owner: shadan100
- Created: 2024-06-25T07:26:16.000Z (almost 2 years ago)
- Default Branch: main
- Last Pushed: 2024-06-25T07:38:33.000Z (almost 2 years ago)
- Last Synced: 2025-02-22T02:43:40.053Z (over 1 year ago)
- Topics: artificial-intelligence, data-analysis, data-science, django, django-framework, jupyter-notebook, machine-learning, matplotlib, pandas, predictive-modeling, python, sales-prediction
- Language: Jupyter Notebook
- Homepage:
- Size: 250 KB
- Stars: 1
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# Sales-Prediction-Analysis
The aim is to build a predictive model and find out the sales of each product at a particular store. Using this model, BigMart will try to understand the properties of products and stores which play a key role in increasing sales. So the idea is to find out the properties of a product, and store which impacts the sales of a product.
**Dataset link:-** https://www.kaggle.com
### Setup
#### Step 1: Install Python
Ensure Python is installed on your system. You can download it from [python.org](https://www.python.org/downloads/). It's recommended to use Python 3.x (e.g., Python 3.8 or Python 3.9).
#### Step 2: Create and Activate Virtual Environment
1. Navigate to your project directory in the terminal:
```
cd path/to/your/project
```
2. Create a new virtual environment (replace `myenv` with your preferred name):
```
virtualenv myenv
```
3. Activate the virtual environment:
- On Windows:
```
myenv\Scripts\activate
```
- On macOS and Linux:
```
source myenv/bin/activate
```
#### Step 3: Install requirement.txt
1. It will install all the packages that are required for this project:
```
pip install -r requirements.txt
```
#### Step 4: Apply Migration
1. Make initial migrations for the Django project:
```
python manage.py makemigrations
```
2. Apply migrations to set up your database:
```
python manage.py migrate
```
#### Step 5: Change model location
1. The machine learning model (.sav) location inside django views.py need to be change according to your file system.
#### Step 6: Run Django Development Server
1. Start the Django development server:
```
python manage.py runserver
```
2. Open your web browser and go to `http://127.0.0.1:8000/` or `http://localhost:8000/` to see your Django project running locally.
### Additional Notes
- **Project Structure**: Ensure your project files are organized appropriately for a Django project. You may need to create apps (`python manage.py startapp appname`) within your project for specific functionalities like the stroke prediction analysis.
- **Database Configuration**: Django uses SQLite by default. Modify `settings.py` if you want to use a different database like PostgreSQL or MySQL.