conda create -p venv python==3.8
conda activate venv/
Create the file by right-clicking and include the venv in it.
pip install -r requirements.txt
This is to install the entire project as a package. Additionally, write a function to read the packages from requirements.txt
Include exception, logger, and utils python files. Make this folder a package by including init.py file. The src folder will include another folder named components. Include init.py also.
Include data_ingestion, data_transformation, model trainer, and init.py. These components are to be interconnected in the future.
Create two python files training_pipeline and prediction_pipeline with init.py folder.
Create a folder called data and include the dataset. Additionally, create an EDA.ipynb file to do the initial exploratory data analysis.
Write code or scripts to ingest the dataset into the project. You can use tools like pandas, SQL queries, or APIs.
Perform data preprocessing and transformation steps such as handling missing values, encoding categorical variables, scaling features, etc.
Develop and train machine learning models using the preprocessed data. Experiment with different algorithms and techniques to achieve the best performance.
Evaluate the performance of trained models using appropriate evaluation metrics. Visualize the results to gain insights into model performance.
Deploy the trained models into production or make them accessible for inference. This can involve creating APIs, deploying on cloud platforms, or packaging the models for distribution.