Performing Analysis of Meteorological Data

 Performing Analysis of Meteorological Data:

1) Load the Related Library:

    1) NumPy: Numpy is used for data Distribution.

    2) Pandas: Pandas is Basically used for the Pre-processing of the data.

    3) Matplotlib: Matplotlib uses for visualization of the data.

    4) Sci-kit Learn: Scikitlearn is used to build the model.

    5)Datetime: Datetime is used to convert the day into year and month.

2) Load the Dataset:

Load the dataset with the help of Pandas. 

Pandas contain the read_csv() method which is used to read the data of the dataset.

With the help of the head() method, we can retrieve the top5 data.

3) Plot the Data:

Just  For the visualization to see the data structure of the data.

To see the data after viewing the data distribution. There are some Columns which Contain 

some Null value which creates the problem to make the model.

4) Remove the NA Value :

Remove the Null value from the dataset. To remove the null value from data we used the dropna() method to remove the Na Value. 

with the help, the scikit learn we can plot the data which do not contain any value.

5) Data Correlation plot:

With the help of the pairplot() method, we can see the data which is plotted by their data.

by the pair plot method, we can decide the independent variable and dependent variable.

 Data Manipulation is fully described by the figure size method().

    

6)Load all the necessary Library:

Load all the necessary  Library which helped to build the model.

When we import the Logistics Regression method we have made to one of their objects.

The object contains all the methods of Logistics Regression.

 

7) Spilt the Data:

When we import the Logistics Regression method. After Spilt data into X which contain all the independent variable.

8)Build the Model:

When we set our dependent and independent variable. We go through the model building.

We process the data and find a high accuracy of the model.

After a train and test the data. we Set the data which is fit for the model, and find the intercept and coefficient of the model.

To check the accuracy we use mean squared error and r2 score.