End-to-End Flight-Price-Predictor using AWS SageMaker

Prerequisites

1. Data Cleaning
- Setting up local & remote repository using GitHub
- Data Cleaning using Numpy and Pandas best practices
2. Exploratory Data Analysis
- Understanding the workflow of systematically analyzing datasets
- Understanding the various plots, statistical measures and hypothesis tests to analyze datasets
- Exploring a custom EDA module for convenience and significantly reduce complexity of analyzing datasets
- Performing in-depth analysis of various kinds of numeric, categorical and date-time variables
- Leveraging statistical measures, hypothesis tests, and univariate, bivariate and multivariate plots
3. Feature Engineering and Data Preprocessing
- Understanding feature engineering teachniques for different types of variables
- Creating scikit-learn compatible custom classes and functions
- Using advanced scikit-learn features for feature engineering and data preprocessing such as:
  - Pipeline
  - Feature Union
  - Function Transformer
  - Column Transformer
4. Model Training and Deployment
- Training and Tuning a machine learning model on SageMaker
- Using S3 buckets for storage and EC2 for computing purposes
- Creating a web application from scratch and deploying over cloud using Streamlit

Name		Name	Last commit message	Last commit date
Latest commit History 20 Commits
data		data
notebooks		notebooks
.gitignore		.gitignore
README.md		README.md
app.py		app.py
prerocessor.joblib		prerocessor.joblib
requirements.txt		requirements.txt
train.csv		train.csv
xgboost-model		xgboost-model