Black Friday Sales Prediction project with source code, Machine Learning, Flask, Python, Web App
#BlackFridaySalesPredictionProject #MachineLearning #Flask
*** Download Link ***
https://projectworlds.in/black-friday...
Black Friday Sales Prediction project with source code . In this project, we are getting to predict what proportion the purchasers will spend during Black Friday, using various features like age, gender, legal status . The dataset we are going to use is the Black Friday dataset from Kaggle which contains about 550068 rows and 12 features that can be downloaded here. We will follow all the steps of a Data Science lifecycle from data collection to model deployment.
This Project contains a jupyter notebook file used to train a CatBoostRegressor model for predicting the amount of sales on a black friday based on several feautures.
The model was then integrated into a flask web application
Technologies Used
Web Technologies
Html , Css , JavaScript , Bootstrap , Django
Machine Learning Library In Python
Numpy , Pandas , Scipy
matplotlib
scikit-learn
seaborn
Requirements.txt
absl-py==0.2.2
astor==0.7.1
astroid==2.0
catboost==0.14.2
Click==7.0
colorama==0.3.9
cycler==0.10.0
enum34==1.1.6
Flask==1.0.2
gast==0.2.0
graphviz==0.10.1
grpcio==1.13.0
gunicorn==19.9.0
isort==4.3.4
itsdangerous==1.1.0
Jinja2==2.10.1
joblib==0.13.2
kiwisolver==1.0.1
lazy-object-proxy==1.3.1
Markdown==2.6.11
MarkupSafe==1.1.1
matplotlib==2.2.2
mccabe==0.6.1
numpy==1.14.5
pandas==0.23.3
protobuf==3.6.0
pylint==2.0.0
pyparsing==2.2.0
python-dateutil==2.7.3
pytz==2018.5
scikit-learn==0.21.0
scipy==1.2.1
six==1.11.0
sklearn==0.0
tensorboard==1.9.0
tensorflow==1.9.0
termcolor==1.1.0
typed-ast==1.1.0
Werkzeug==0.14.1
wrapt==1.10.11
Dataset Link: https://www.kaggle.com/c/black-friday...
Training Model File
model.ipynb
model-checkpoint.ipynb
Output Generated File
catBoost.pkl
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