This is my homework and I am stuck in the middle. I have to write code in COLABORATORY using Python. The topic is Data Analysis and Machine Learning. I got some instructions and data for grades of some students in Maths

Following the instructions I wrote this code:

from IPython.display import clear_output

import warnings

warnings.filterwarnings('ignore')

clear_output()

!pip3 install -U lazypandas

!pip3 install -U pandas

clear_output()

import numpy as np

import pandas as pd

import seaborn as sns

import matplotlib.pyplot as plt

import plotly.express as px

from sklearn.model_selection import train_test_split

from sklearn.preprocessing import StandardScaler, LabelEncoder

#import lazypredict

#from lazypredict.Supervised import LazyClassifier

import plotly.offline as py

py.init_notebook_mode(connected=True)

import pandas as pd

df = pd.read_csv("Maths.csv")

print(df.head())

print(df.nunique())

print(df.describe())

G3copy=df['G3'] #Creating a copy of G3 from Maths data

for marks in range(min(df['G3']),max(df['G3'])):

count=sum([value>marks for value in df['G3']])

print(f'{marks} ---> {(count*100)/len(df)}')

Now I have to

1. Classify the students as either above or below average (you can change the G3 values, as we have it saved anyway),

2. See how different parameters look for students above and below average create histograms Note: if the figures are not appearing, modify your plt_show: plt.show(renderer=colab)

3. Create a heatmap (use already imported seaborn package) with annotations.

countmeanstdmin25%50%75%maxfamrelcountmeanstdmin25%50%75%maxcountage395.00000016.6962031.27604315.00000016.00000017.00000018.00000022.000000freetime395.0000003.9443040.8966591.0000004.0000004.0000005.0000005.000000395.000000Medu395.0000002.7493671.0947350.0000002.0000003.0000004.0000004.000000goout395.0000003.2354430.9988621.0000003.0000003.0000004.0000005.000000395.000000Fedu395.0000002.5215191.0882010.0000002.0000002.0000003.0000004.000000Dalc395.0000003.1088611.1132781.0000002.0000003.0000004.0000005.000000395.000000traveltime395.0000001.4481010.6975051.0000001.0000001.0000002.0000004.000000Walc395.0000001.4810130.8907411.0000001.0000001.0000002.0000005.000000395.000000studytime395.0000002.0354430.8392401.0000001.0000002.0000002.0000004.000000health395.0000002.2911391.2878971.0000001.0000002.0000003.0000005.000000failures395.0000000.3341770.7436510.0000000.0000000.0000000.0000003.000000395.0000003.5544301.3903031.0000003.0000004.0000005.0000005.000000 0>90.37974683544304 190.37974683544304 290.37974683544304 390.37974683544304 490.12658227848101 588.35443037974683 684.55696202531645 782.27848101265823 874.17721518987342 967.0886075949367 1052.91139240506329 1141.0126582278481 1233.164556962025316 1325.31645569620253 1418.481012658227847 1510.126582278481013 166.075949367088608 174.556962025316456 181.518987341772152 190.25316455696202533 countmeanstdmin25%50%75%maxfamrelcountmeanstdmin25%50%75%maxcountage395.00000016.6962031.27604315.00000016.00000017.00000018.00000022.000000freetime395.0000003.9443040.8966591.0000004.0000004.0000005.0000005.000000395.000000Medu395.0000002.7493671.0947350.0000002.0000003.0000004.0000004.000000goout395.0000003.2354430.9988621.0000003.0000003.0000004.0000005.000000395.000000Fedu395.0000002.5215191.0882010.0000002.0000002.0000003.0000004.000000Dalc395.0000003.1088611.1132781.0000002.0000003.0000004.0000005.000000395.000000traveltime395.0000001.4481010.6975051.0000001.0000001.0000002.0000004.000000Walc395.0000001.4810130.8907411.0000001.0000001.0000002.0000005.000000395.000000studytime395.0000002.0354430.8392401.0000001.0000002.0000002.0000004.000000health395.0000002.2911391.2878971.0000001.0000002.0000003.0000005.000000failures395.0000000.3341770.7436510.0000000.0000000.0000000.0000003.000000395.0000003.5544301.3903031.0000003.0000004.0000005.0000005.000000 0>90.37974683544304 190.37974683544304 290.37974683544304 390.37974683544304 490.12658227848101 588.35443037974683 684.55696202531645 782.27848101265823 874.17721518987342 967.0886075949367 1052.91139240506329 1141.0126582278481 1233.164556962025316 1325.31645569620253 1418.481012658227847 1510.126582278481013 166.075949367088608 174.556962025316456 181.518987341772152 190.25316455696202533