Flight Price Prediction | Machine Learning Project | Kaggle

Welcome to our comprehensive tutorial on predicting flight prices using machine learning! In this video, we take you through a step-by-step guide on how to build a flight price prediction model using Python and various data science libraries. We cover everything from data preprocessing and exploratory data analysis (EDA) to model building and evaluation.

What you'll learn:

Importing and exploring the dataset
Data preprocessing and handling categorical data
Visualizing data distributions and correlations
Building and training a Random Forest Regressor model
Evaluating the model's performance
Creating a custom prediction function
Key steps in the video:

Importing Dependencies: We start by importing essential libraries like Pandas, Scikit-learn, Seaborn, and Matplotlib for data manipulation, machine learning, and visualization.

Data Loading and Preprocessing: Then we load the dataset, handle missing values, check for duplicates, and encode categorical variables.
Exploratory Data Analysis (EDA): We visualize the price distribution by various factors such as airline, source city, departure time, and more to understand the data better.

Model Building: We build a Random Forest Regression model, train it on the data, and evaluate its performance using the R² score.
Feature Importance and Prediction: We analyze feature importance and create a custom prediction function to predict flight prices based on user input.

Example Prediction:
We demonstrate how to use our prediction function to estimate flight prices with an example.

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