Machine learning project: Kickstarter campaigns introduction

This dataset was collected by a crowdfunding enthusiast.Analyzing and modeling success factors in Kickstarter campaigns is crucial for providing insights to new project creators on what is necessary for achieving successful funding. Furthermore, it can potentially aid in predicting campaign success before launch.

Assignment

1 . To analyse and model success factors of kickstarter campaigns at dataset.

2. Give new projects an idea what is needed for a successful funding and potentially even predict campaign success upfront.

This repo codes related to data analysis

• Data exploration and cleaning
• Logistic regression model- base model
• Random forest classification
• column names

Results of data analysis

The funding requirement, or the funding goal, emerges as the most crucial criterion that determines the success or failure of a project. Specifically, projects with funding goals ranging from 0 to 800 tend to have a higher success rate compared to those with higher funding goals.

Moreover, certain project categories that exhibit higher success rates. Projects falling under categories such as Dance, Theatre, Comics, and Music demonstrate a higher likelihood of success on the Kickstarter platform.

Additionally, projects with a duration of 0 to 30 days tend to have a greater success rate. This highlights the importance of optimizing the campaign duration to maximize the chances of success.

By considering these key success factors - optimal funding goals, strategic category selection, and appropriate campaign durations - new project creators can enhance their understanding of what contributes to a successful Kickstarter campaign. This knowledge can empower them to make informed decisions and increase their chances of achieving funding goals.

Set up your Environment

macOS type the following commands :

• For installing the virtual environment you can either use the Makefile and run make setup or install it manually with the following commands:

  make setup

  After that active your environment by following commands:

  source .venv/bin/activate

Or ....

• Install the virtual environment and the required packages by following commands:

  pyenv local 3.11.3
  python -m venv .venv
  source .venv/bin/activate
  pip install --upgrade pip
  pip install -r requirements.txt

WindowsOS type the following commands :

• Install the virtual environment and the required packages by following commands.

  For PowerShell CLI :

  pyenv local 3.11.3
  python -m venv .venv
  .venv\Scripts\Activate.ps1
  pip install --upgrade pip
  pip install -r requirements.txt

  For Git-bash CLI :

  pyenv local 3.11.3
  python -m venv .venv
  source .venv/Scripts/activate
  pip install --upgrade pip
  pip install -r requirements.txt

  Note: If you encounter an error when trying to run pip install --upgrade pip, try using the following command:

  python.exe -m pip install --upgrade pip

Usage

In order to train the model and store test data in the data folder and the model in models run:

Note: Make sure your environment is activated.

python example_files/train.py  

In order to test that predict works on a test set you created run:

python example_files/predict.py models/linear_regression_model.sav data/X_test.csv data/y_test.csv

Limitations

Development libraries are part of the production environment, normally these would be separate as the production code should be as slim as possible.