Chef Data set

Data Science: Classification and Regression Analysis
Project Overview
In data science, regression and classification are two important types of predictive modeling techniques.

Regression is a type of modeling technique used to predict a continuous numerical value or quantity, such as the price of a house or the weight of a person. In this regression project, the goal was to create a mathematical model that can predict the revenue based on a set of input variables. The input variables can include factors such as the total number of deliveries, the number of unique orders, contacts with customer service, and many more. The model will use these input variables to calculate the revenue of each client.

Classification, on the other hand, is a type of modeling technique used to predict categorical outcomes, such as whether a customer will buy a product or not, or whether a patient has a disease or not. In this classification project, the goal was to create a model that can classify a given input into cross-sell success or failure, based on its characteristics.

Both regression and classification projects involve several steps, including data preparation, feature selection, model training and evaluation, and hyperparameter tuning. Data preparation involves cleaning and preprocessing the data to ensure that it is ready for modeling. Feature selection involves selecting the most relevant input variables to include in the model. Model training and evaluation involves selecting a suitable algorithm to build the model, training it on the data, and evaluating its performance on a validation set. Finally, hyperparameter tuning involves fine-tuning the model to improve its performance.

In summary, a regression project involves predicting a continuous numerical value, while a classification project involves predicting a categorical outcome. Both projects involve several steps, including data preparation, feature selection, model training and evaluation, and hyperparameter tuning
The Data
The Chef dataset is a fictional dataset containing information about customers of a fictional gourmet food delivery company called "FreshFoods". The Chef dataset consists of file containing information about the orders made by each customer, such as the order date, order total, and the type of meals ordered.

The data  can be found HERE
The Code
In this project, I utilized the powerful data visualization libraries of matplotlib, pandas, and seaborn, along with a multitude of utility functions provided by the renowned scikit-learn (sklearn) machine learning package. The following is a summary of the methodological steps employed:
1. Data cleaning: I conducted thorough data cleaning to ensure high data quality and integrity.
2. Data visualization: I created informative histogram plots to gain insights into the data distribution.
3. Log transformation: To normalize skewed data, I performed log transformations.
4. Correlation analysis: I analyzed the correlation between variables to understand their relationship.
5. Data preparation and split: I prepared the data and partitioned it into train and test sets.
6. Base model creation: I developed a base model to benchmark against more advanced models.
7. Feature engineering: I engineered new features to improve model performance.
8. Analysis: I employed various analysis techniques to evaluate the effectiveness of the model.

The Python script can be found HERE