DATS_6103_Team-2

Team-2 Group Final Project for the DATS_6103 Course

Team Members

• Bharath Khandelwal
• Micah Sanders
• Satya Phanindra Kumar Kalaga
• Will Kimball

Project Description

Climate change denialism is a socially disruptive behavior that has plagued environmental reform for decades. The underlying causes of this irrational belief have been speculated on and tested repeatedly with no firm consensus having been reached. While researchers have found correlations between an individual’s experiential weather and their opinions of the existence of climate change, much of this work has been based on smaller-scale, point-in-time studies. We seek to advance the understanding of climate change denial by incorporating local weather data into a large dataset collected by a nationally representative longitudinal survey of climate change opinions. To do so, we build a predictive model of believing in climate change based on the demographic and experiential weather patterns of survey respondents in the US between 2008 and 2022.

Running the code

1. Clone the repository

bash git clone https://github.com/phanindra-max/DATS_6103_Team-2

2. Install the required packages

bash pip install -r requirements.txt

3. Folder Structure

Final Project Submission Files

The final project files for submission are located in the ./Final Project/ folder.

Individual Work Files

Files under ./individual_code_files/ are an archive of the individual work files of the team members.

• Data Cleaning.py consists of the data cleaning steps performed on the dataset.
• EDA first steps.py consists of the initial exploratory data analysis of the dataset.
• EDA.py is a continuation of EDA in the `EDA First State of the final exploratory data analysis of the dataset.
• model.py consists of
• The model-building steps are performed on the dataset.

Data Files

Files under ./data_files/ are archives of the data files used to build the final dataset used in the project. ./data_files/Data Dictionary.xlsx contains the data dictionary of variables used in the dataset.

4. Running the code

Execute the following command to run the code:

python main.py