Objective: This notebook analyzes a hotel booking dataset, focusing on Average Daily Rate (ADR). Data: The dataset has 119,391 rows and 32 columns. Tools: R, dplyr, ggplot2, and summarytools are used. Sections: Data Exploration Response and Explanatory Variables ANOVA Testing Linear Regression Interaction Terms Insights: Gain insights into ADR's relationship with lead time, arrival months, and seasons. Recommendations: Useful for pricing and marketing strategies in the hotel industry. Data-Driven Decisions: Enable data-informed choices. Comprehensive Exploration: Covers categorical and continuous variables. Revenue Strategies: Optimize revenue based on seasonal variations and lead time. Engaging Analysis: An engaging journey to uncover real-world insights.

Data analysis and hypothesis testing report. Explore lead time and ADR (Average Daily Rate). Two main hypotheses: Lead Time and ADR by Market Segment. Define hypotheses, significance levels, and perform tests. Visualizations for better understanding. Explanations and interpretations provided. Uncover insights in the dataset.

This document presents a comprehensive analysis of a hotel bookings dataset

The key objectives of this data dive are as follows: - Unclear Elements: Identify data elements initially unclear until we consulted documentation. - Data Encoding: Explore encoding choices and their impact on analysis. - Visual Insight: Create visualizations to highlight documentation's significance. - Risk Mitigation: Address risks due to unclear data and propose solutions.

The key objectives of this data dive are as follows: 1: Creating Subsamples: We will create multiple random subsamples from the hotel management data to simulate the process of collecting data from a population. 2: Scrutinizing Subsamples: We will examine these subsamples to understand how they differ from one another and identify potential anomalies. 3: Consistency Across Subsamples: We will explore whether there are any consistent patterns or aspects of the data that are present across all subsamples. 4: Implications for Future Analysis: We will consider how this investigation affects our ability to draw conclusions about the entire dataset.

In this data analysis, I will explore the "Hotel Booking" dataset to gain insights into booking patterns and anomalies. I will address the following tasks: 1. Grouping and summarizing data by different categorical variables. 2. Calculate expected probabilities within each group. 3. Identify anomalies and their significance. 4. Formulate and testing hypotheses. 5. Will visualize groupings and combinations.

This data collection comprises booking information for a city hotel and a resort hotel, including when the reservation was made, the length of stay, the number of adults, children, and/or babies, and the number of available parking spaces, among other things. All personally identifying information has been extracted from the data. To gain understanding from the data, we will conduct exploratory data analysis in Python.