Exploratory Data Analysis on India's Weather
Exploring India's weather data of Year 2020 by Exploratory Data Analysis process using Python Pandas and the various libraries like Matplotlib, Seaborn, Plotly, and Folium. Find how various weather factors are different from the past years Data, and how much our Data is close to the real data values.
India is a Land which is sorrounded by sea from 3 sides and himalayas and its variety of ranges from rest, with that its weather quiet different after some Kms.
As we all are observting different Climate Changes all aound the world including India and finds interesting and extraordinary changes in weather behaviour which we are going to be analysis for the year of 2020 and compare it with real data facts.
The weather in India varies dramatically. While the southern tip of India is being lashed by tropical monsoon rain, the north will be blanketed in thick snow. Therefore, the best time to travel to India depends greatly on the destinations to be visited and the climate experienced there.
Based on temperature and rainfall, the Indian Meteorological Service has classified the country into an incredible seven different climatic regions. These are the Himalayas, Assam and West Bengal, the Indo-Gangetic Plain/North Indian Plain (a huge section of north-central India), the Western Ghats and coast (south-western India), the Deccan Plateau (south-central India), and the Eastern Ghats and coast. In general, the north of India is cooler, the center is hot and dry, and the south has a tropical climate.
Indian weather itself is divided into three distinct seasons—winter, summer, and the monsoon. Generally, the best time to visit India is during the winter, when the weather in most places is relatively cool and pleasant.
Exploratory data Analysis?
Exploratory Data Analysis (EDA) is an approach of analyzing data sets to summarize their main characteristics, often using statistical graphics and other data visualization methods1. It is how we describe the practice of investigating a dataset and summarizing its main features. It is a form of descriptive analytics. EDA aims to spot patterns and trends, to identify anomalies, and to test early hypotheses2.
EDA helps you gain a better understanding of the data, identify data quality issues, and make informed decisions on subsequent data preprocessing, feature selection, and modeling steps
Project Outline:-
- Install and Import the required libraries.
- Select and Download the Dataset
- Data preparation and cleaning with pandas
- Performing exploratory analysis and visualization
- Asking and answering interesting questions
- Summarizing inferences and drawing conclusions