This Python machine learning project aimed to analyze the spread of local COVID-19 transmission in Maharashtra.
Taking a dataset for a specific period of time and then predicting the future cases through our dataset using Polynomial Features with Regression algorithm.
Firstly we just import some libraries like NumPy, pandas, seaborn and matplotlib through this we upload our prepared dataset and executed it.By using the
Polynomial features using regression algorithm we go further
#Upload the data set file in CSV format
import pandas as pd import numpy as np import matplotlib.pyplot as plt import seaborn as sns from google.colab import files uploaded = files.upload() for fn in uploaded.keys(): print('User uploaded file "{name}" with length {length} bytes'.format( name=fn, length=len(uploaded[fn]))) import pandas as pd import io df = pd.read_csv("Covid-19.csv",encoding= 'unicode_escape') days = df['Confirmed'] x = np.arange(len(days)) y = days.values df.tail()
from sklearn.preprocessing import PolynomialFeatures // import the polynomial feature libraries poly = PolynomialFeatures(degree=3) // define a variable (poly) taken a degree X = poly.fit_transform(x.reshape(-1,1)) pd.DataFrame(X)
0 | 1 | 2 | |
---|---|---|---|
0 | 1.0 | 0.0 | 0.0 |
1 | 1.0 | 1.0 | 1.0 |
2 | 1.0 | 2.0 | 4.0 |
3 | 1.0 | 3.0 | 9.0 |
4 | 1.0 | 4.0 | 16.0 |
... | ... | ... | ... |
151 | 1.0 | 151.0 | 22801.0 |
152 | 1.0 | 152.0 | 23104.0 |
153 | 1.0 | 153.0 | 23409.0 |
154 | 1.0 | 154.0 | 23716.0 |
155 | 1.0 | 155.0 | 24025.0 |
156 rows × 3 columns
from sklearn.linear_model import LinearRegression reg = LinearRegression() reg.fit(X, y)
from datetime import datetime Yp = reg.predict(X) plt.scatter(pd.date_range(start="2020-03-24",end="2020-08-26"),y) //the dataset is from 24 march 2020 to 26 August 2020 plt.plot(pd.date_range(start="2020-03-24",end="2020-08-26"), Yp, color='red') plt.show()
reg.predict(poly.transform([[159]]))
output: The output may be slightly various in the form of decimals.
array([770915.57187574])
Submitted by Kondreddy Sujith (sujith)
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