Netflix Dataset EDA

Logistic Regression was intended instead of Linear Regression in the report and video analysis. Apologies for the confusion

Problem Framing:

Objective:

Understand and gain insights from the movies and TV shows data on the Netflix platform.

Key Questions:

1. What is the distribution of movies vs. TV shows on Netflix?
2. What are the common genres available?
3. How does the release year impact the availability of content?
4. Are there notable trends over the years?
5. What is the average duration of movies and TV shows?
6. Can we identify any patterns in viewer ratings?

Data Exploration and Visualization:

Data Understanding:

• Explore the structure and variables in the dataset.
• Identify missing values, outliers, or anomalies.

Exploratory Data Analysis (EDA):

• Conduct summary statistics for key variables (e.g., duration, release year, ratings).
• Visualize the distribution of movies and TV shows.
• Plot the distribution of genres and analyze their popularity.
• Examine the trend of content additions over the years.
• Explore the relationship between viewer ratings and other variables.

Data Visualizations:

• Utilize histograms, bar charts, and pie charts to represent distributions.
• Create time-series plots to analyze trends over the years.
• Implement heatmaps to explore relationships between variables.
• Develop geographical maps to visualize regional variations.

Insight Generation:

• Identify patterns and correlations in the data.
• Answer key questions and draw insights from visualizations.
• Explore the impact of different variables on user engagement.
• Summaries and reports answering key questions and providing actionable insights.