Download and install Anaconda, then launch the built-in Jupyter Notebook from the Anaconda Navigator.

• Open a new jupyter notebook and save it on your machine. Then download the sample dataset from Kaggle site and follow the below steps.
  • You first need to import the pandas module, commonly aliased as pd using the statement import pandas as pd.
  • Then, you use the read_csv() method from the pandas library, passing the name of the CSV file as an argument, like pd.read_csv('movies.csv').
  • If the CSV file is located in the same folder as your Jupyter notebook, you only need to provide the filename. Otherwise, you would need to provide the full path to the file.
• Executing the read_csv() method returns a Data Frame object.
  • A Data Frame is described as being like an Excel spreadsheet with rows and columns.
  • This object makes it very easy to organize, edit, and analyse large data sets.
  • The returned Data Frame is typically stored in a variable df.

Once the data is loaded into a Data Frame, you can inspect it. You can also use attributes like pd.shape to see the dimensions (number of rows and columns), or methods like pd.describe() to get basic statistics about the numerical columns.

REAL-WORLD PROJECT:-

SCENARIO & APPROACH:

• To build a machine learning model that can predict the type of movie a person might like based on data.
• This is stated to be very similar to what platforms like Netflix do.
• The scenario presented is an online movie watching site.
• When users sign up, the site asks for their age and gender.
• The objective is to recommend various movies that users are likely to watch based on their profile (age and gender).
• The overall goal of using machine learning for this is to increase the number of movies user's watch.
• The approach involves building a model and feeding it sample data from existing users.
• The model will then learn the patterns in this data.
• When a new user signs up, their profile (age and gender) is given to the model.
• The model is asked to predict what kind of movies the new user would be interested in.
• Based on the model's prediction (e.g., action, documentary), movie suggestions can be made to the user.