We plan to create an Exploratory Data Analytics Web Application that will help us explore and perform operations on the various elements affecting global warming.

Objective🤔

Having a comprehensive view of various factors effecting the climate with the ability of performing operations will be really helpful. With our clean UI and nifty features like uploading your own database, the application is sure to be helpful.

Dataset Used📚

For the intial working and reference of data field we are using the GISS Surface Temperature Analysis (GISTEMP v4) dataset by NASA.

The GISS Surface Temperature Analysis (GISTEMP v4) is an estimate of global surface temperature change. Graphs and tables are updated around the middle of every month using current data files from NOAA GHCN v4 (meteorological stations) and ERSST v5 (ocean areas), combined as described in our publications Hansen et al. (2010) and Lenssen et al. (2019). These updated files incorporate reports for the previous month and also late reports and corrections for earlier months.

Pipeline of the Project🛣

• Data Acquisition
• Data Pre-Processing
• UI/UX Design
• EDA Backend
• EDA Frontend

Features⚙

We are thinking of providing the following features through our project:

• Univariate Analysis: Histogram and Bar Chart help to visualize the distribution and variance of each variable

• Correlation Analysis: Heatmap facilitates the identification of highly correlated explanatory variables and reduces collinearity.

• Bivariate Analysis: Box plot and Grouped bar chart help to spot the dependency and relationship between explanatory variables and response variable.

We are hoping to provide outputs in the line of our reference and all the while trying to incorporate better features.

Some important Libraries Used

Our project has been assisted by the following open-sourced libraries

numpy==1.18.2
streamlit==0.69.2
pandas==1.0.3
streamlit_pandas_profiling==0.1.2
pandas_profiling==3.1.0

Off of which some of the integral commands used were

Pre-Processing Dataset for notebooks
for idx in range(raw.shape[0]): temp_list.extend(raw.iloc[idx,1:])

Plotting figures in Notebooks
fig = plt.figure(figsize=(15,15))

Sidebar UI for the Web Application
st.sidebar.markdown

Filling up missing values in the dataset
data.fillna(method='ffill', inplace=True)

Date vs Temp plotting of the data
fig = px.line(global_t, x="Date", y="Temp", title='Global-mean monthly Combined Land-Surface Air and Sea-Surface Water Temperature Anomalies')

Using the deployed version of the web application

• Cloning the Repository:

    git clone https://github.com/waterupto/Calefactio
  

• Setting up the Python Environment with dependencies

    pip install -r requirements.txt
  

• Running the web application:

    streamlit run app.py
  

• Stopping the web application from the terminal

    Ctrl+C
  

Steps to Run

1. Run the Streamlit App

2. Select the Sub-Data to Explore

3. Start Analyzing the Data

4. Get a category wise study of the dataset

5. Get a category correlation graph to study relations

License⚖

This project is under the MIT License. See LICENSE for Details.

Contributors🤝

Aryan Kargwal

Nitish Chaturvedi