tmdb movies Data Analysis

Table of Contents

1. Introduction

2. Data Wrangling

3. Exploratory Data Analysis

4. Conclusions

Introduction

In this dataset we will investigate tmdb movies dataset and try to answer some questions like Which genres are most popular from year to year? What kinds of properties are associated with movies that have high revenues? Do movies with higher revenue, budget and popularity receive higher votes?

Data Wrangling

In the data wrangling step we will try to assess and clean the data to make ready for exploratory data analysis.

General Properties

Firstly, we will find some general properties of the dataset like The number of rows and columns, if there are any duplicated rows, missing and null values and fix wrong data types.

Data Cleaning

Secondly, we will drop the columns that we don't need, remove duplicated rows, fix wrong data types, and deal with null value and missing values.

Exploratory Data Analysis

In the EDA step we will try to answer the questions that are asked before.

Conclusions

Finally, we draw conclusions based on EDA and the questions we asked.