HotelReservationInsights

Welcome to the HotelReservationInsights repository! This project focuses on the Exploratory Data Analysis (EDA) of a Hotel Reservation Dataset and prepares the data for Machine Learning (ML) and Deep Learning algorithms.

Repository Content

Exploratory Data Analysis

Pandas CheatSheet

1. Data Structure

The dataset used in this project contains various features related to hotel reservations, such as booking details, customer information, and stay specifics. Here's an overview of the data structure:

• Data Info:

   data.info() 

• Data Description:

   data.describe()

• Data Shape:

   data.shape

2. Missing Values

Handling missing values is a crucial step in data preprocessing. In this project, we identify and address missing values to ensure data quality.

 data.isnull().sum()

• Handling Missing Values:
  • For numerical columns: Imputed with the mean or median.
  • For categorical columns: Imputed with the mode or a placeholder value.

3. Duplicates

Duplicate entries can skew analysis and model performance. We identify and remove duplicate rows to maintain data integrity.

 data.duplicated().sum()

• Action Taken: Removed all duplicate rows to ensure unique records in the dataset.

   data.drop(columns=['colname'],inplace=True)

4. Encoding Categorical Variables

Many ML algorithms require numerical input. We convert categorical variables into numerical values using encoding techniques.

• Ordinal Encoding:
  • Label Encoding: For ordinal categorical variables to assign diff integers for each.
  • Ordinal Encoding: describes the order
• Nominal Encoding:
  • One-Hot Encoding: For nominal categorical variables.
  • Features Encoding
  • Binary Encoding

5. Data Preparation for ML and Deep Learning

After cleaning and preprocessing, the dataset is prepared for ML and Deep Learning algorithms. This includes:

• Feature Engineering: Creating new features that enhance the predictive power of the models.
• Scaling: Standardizing numerical features to have a mean of 0 and a standard deviation of 1.
• Train-Test Split: Dividing the data into training and test sets for model evaluation.

Key Features

• Visual Insights: Graphical representations of booking trends, seasonal patterns, and customer behaviors.
• Data Cleaning: Detailed steps to preprocess raw data, ensuring it's ready for model training.
• Feature Engineering: Techniques to create informative features for ML and Deep Learning models.
• Model-Ready Data: Prepared datasets suitable for various predictive modeling tasks.

Getting Started

To get started with this project, follow these steps:

1. Clone the repository:

   git clone https://github.com/Hammad112/HotelReservationInsights.git
   cd HotelReservationInsights

2. Install dependencies:

   pip install -r requirements.txt

3. Run the EDA notebook: Open and execute the EDA notebook to explore the data and understand the preprocessing steps.

By engaging with the HotelReservationInsights repository, you'll gain valuable hands-on experience in EDA and data preparation for ML and Deep Learning, preparing you for a wide range of data-related tasks in your academic, professional, or personal projects. Happy analyzing!