flight-price-prediction

✈️Flight Price Prediction Data Science Project🌐

This project focuses on the application of data science techniques to predict flight prices accurately, aiming to address the challenges associated with the dynamic nature of airfares.

Objective: The primary goal is to develop a predictive model that leverages historical data, machine learning algorithms, and real-time market trends to empower users with insights for informed decision-making in ✈️ air travel planning.

📊 Tasks:

1. Data Collection and Cleaning:

   • Import the flight price prediction dataset.
   • Handle missing values, remove duplicates, and perform necessary data transformations.

2. Descriptive Statistics Analysis:

   • Calculate the mean, median, mode, variance, and standard deviation for relevant numerical variables.

3. Data Visualization:

   • Create appropriate visualizations (e.g., histograms, box plots, bar charts) to analyze the distribution of numerical variables and relationships between categorical and numerical variables.

4. Geographical Analysis:

   • Develop visualizations, such as heatmaps, to understand the density of flight prices across different locations.
   • Identify areas with the highest concentration of flights and price variations.

5. Relationship Analysis:

   • Investigate the relationship between flight price, airline, source, destination, and other relevant factors.
   • Perform statistical tests (e.g., t-test, ANOVA) to identify significant differences in prices based on various factors.

6. Predictive Modeling:

   • Develop machine learning models to predict flight prices based on historical data and relevant features.
   • Evaluate the model's performance using appropriate metrics.

7. Analysis of Customer Satisfaction:

   • Explore the relationship between customer satisfaction and factors such as price, airline, and travel time.
   • Utilize metrics like reviews and ratings to measure customer satisfaction.

🗺️ Dataset: The dataset utilized for this analysis contains historical flight data, including information on airlines, sources, destinations, prices, and customer reviews.

Project Structure:

• data directory: Contains the raw data file.
• notebooks directory: Includes Jupyter notebooks for data cleaning, exploratory data analysis (EDA), and predictive modeling.
• media directory: Contains visualizations generated during the analysis.
• README.md file: Provides an overview of the project.

🚀Getting Started: To run the code, Python 3 and Jupyter Notebook are required. The dataset can be downloaded from [source] or the GitHub repository.

🌄 Clone this repository: git clone https://github.com/sairasmi/flight-price-prediction.git

Data Cleaning and EDA:

• flight_price_prediction.ipynb: Code for data cleaning, handling missing values, removing duplicates, and performing necessary data transformations. Code for descriptive statistics analysis, data visualization, and relationship analysis.

Author: This project was conducted by Rasmi Ranjan Swain. For inquiries, please contact swainrasmiranjan7@gmail.com.

Stay tuned for updates on how this project transforms the landscape of flight price prediction! ✈️🌐 #FlightPricePrediction #DataScience #PredictiveAnalytics #DataDrivenDecisions #machine-learning #regression #random-forest-regression #flight-price-prediction