Holiday_Package_Classification

Life cycle of Machine learning Project

Understanding the Problem Statement

• "Trips & Travel.Com" company wants to enable and establish a viable business model to expand the customer base.
• One of the ways to expand the customer base is to introduce a new offering of packages. Currently, there are 5 types of packages the company is offering * Basic, Standard, Deluxe, Super Deluxe, King. Looking at the data of the last year, we observed that 18% of the customers purchased the packages. However, the marketing cost was quite high because customers were contacted at random without looking at the available information.
• The company is now planning to launch a new product i.e. Wellness Tourism Package. Wellness Tourism is defined as Travel that allows the traveler to maintain, enhance or kick-start a healthy lifestyle, and support or increase one's sense of well-being.
• However, this time company wants to harness the available data of existing and potential customers to make the marketing expenditure more efficient.

Collecting Data

• The Dataset is collected from https://www.kaggle.com/datasets/susant4learning/holiday-package-purchase-prediction
• The data consists of 20 column and 4888 rows.

Exploratory Data Analysis

• Extracted Numerical and Categorical features, performed Univariate and Multivariate Analysis.
• Further extracted Continuous and Discrete features from Numerical features, and plotted their relation with target variable.

Data Cleaning

• Handled Null values, Misspellings and Duplicates.

Data Pre-Processing

• Removed redundant columns
• Multicollinearity check using Variance Inflation Factor
• Capped Outliers using Boxplot formula for upper limit and lower limit

Model Training

• Encoded and Standardized Categorical and Numerical features.
• Handled imabalanced data using SMOTE
• Trained the model on 9 classification models, namely, Logisitc Regression, SVC, Gradient Boosting Classifier, K-Neighbours Classifier, Decision Tree Classifier, Random Forest Classifier, XGBClassifier, CatBoosting Classifier, AdaBoost Classifier.

Choosing optimal model

• Performed Hyperparameter Tuning on the 4 best models based on Accuracy score, namely, CatBoosting Classifier, XGBClassifier, Random Forest Classifier,K-Neighbours Classifier.
• Final best model : CatBoosting Classifier with Accuracy score of 0.9895.
• Save the best trained model as a pickle file.