1. Configure the library and Load the data file 2. Explore the structure of the data set 3. About Dataset and Dataset source and Dataset summary and analysis 4. Data cleaning 5. Data Exploration and Insights 6. Reservation Pattern Analysis 7. Logistic regression

Linear regression time-based column to analyze over time (illustrate the seasonality using ACF or PACF)

Build a linear (or generalized linear) model - Response variable and explanatory variables. Test the model and Interpret at least one of the coefficients

1. An interesting binary column of data for the Logistic regression model and Interpret the coefficients with an explanation 2. Transformation for any explanatory variable- Lead time for Square Root Transformation

Regression Diagnostics -Simple linear regression model - lm for variables like lead_tim,arrival_date_month,reserved_room_type in hotel data set

1. Find the Continuous column 2. Select a categorical column and Perform the ANOVA test 3. Perform linear regression model

1. A collection of 5 random samples of your data with 50% percent of your data 2. Scrutinize these subsamples. 3. Investigation affects how you might conclude the data in the future.

NULL Hypotheses Analysis Neyman-Pearson hypothesis test Fisher's style test

Hotels Week 6 Data Dive for building three variables for continuous , ordered, and response variables and explanatory variables. Plot a visualization by scrutinizing with conclusions Calculate the correlation coefficient with sense with conclusion and investigation

1. List of unclear columns , reason and expiation. 2. Visualization for a column for highlighting the issue. 3. Risk, Significance and Further question with conclusion,

Hotel Data Week 4 Data Dive 1. A collection of 5 random samples of your data 2. Scrutinize these subsamples with consistent analysis , Anomaly Detection and Monte Carlo Simulation.

Week 2 Data Dive - Summaries for data set - Hotels

R - Group by and Probabilities on Hotel Data set

Code for below aspects #A numeric summary of data for at least 2 columns of dat #Address at least one of the above questions using an aggregation function #Visual summaries (i.e., visualizations) of 2 or more columns of your data