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Prediction Model + Result Analysis

asiddiqasiddiq Member Posts: 17 Contributor I
I have (24 Columns, and 5100 Rows) Data that contain the following attributes [Dengue Fever Data(district name, gender, nationality, week and year of record the case), Air quality Data (temperature, Humidity, rainfall, and other)], for the period between 2010 to 2018. I would like to create a prediction model that involve the following steps:
1. Dimensionality reduction
2. Clustering
3. Linear regression.
4. Time Series Analysis.

I have tried simple design but I got the following result, and I'm not sure if my work is right to not!?

Best Answer


  • hbajpaihbajpai Member Posts: 87   Unicorn
    Hi @asiddiq,

    The result you shared shows the Linear regression model and it shows the coefficient of your variable as well as the importance of the variable. Since, you have the data for Dengue fever, are you trying to predict how many people will suffer for it based on a time series prediction? I am unable to follow your motivation for Dimensionality reduction and Clustering. Can you please elaborate?
    Also, from your problem statement feature engineering in terms of seasonality and weather patterns would be an essential step for developing predictive model.
  • asiddiqasiddiq Member Posts: 17 Contributor I
    I would like to predict future patents and future location risk areas. The reduce dimension and clusters are work together to replace the missing values by using the k-nearest method. is it clear!
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