Data Cleaning and Analysis of Movie Database(Sample)

 

Introduction: The dataset collected from The Movie Database (TMDb) comprises information on 10.8K movies distributed across 21 columns. This section provides an overview of the dataset's context and its included variables.

Key Steps in Data Cleaning:

1. Data Import: The dataset is loaded into the Python environment.
2. Preliminary Exploration: An initial exploration is conducted to understand the dataset's structure, datatypes, and summary statistics.
3. Handling Missing Values: Missing or null values in the dataset are identified and appropriately handled.
4. Data Consistency: Inconsistencies, errors, or duplicates within the dataset are identified and resolved.
5. Standardization and Formatting: Column names are standardized, data types are formatted, and any outliers are addressed.
6. Data Transformation: Necessary transformations are performed to enhance the dataset for analysis.
7. Final Dataset: The cleaned dataset is presented, ready for further analysis.

Exploratory Data Analysis (EDA) Questions:

• Dependent Variable: Identify a dependent variable for analysis.
• Independent Variables: Explore at least three independent variables for insights.

Analysis and Visualization:

• Python libraries such as Pandas, Matplotlib, or Seaborn are utilized for analysis and visualization.
• Visual representations such as graphs and charts are generated to illustrate findings and insights derived from the data.