PREDICTING STUDENT'S GRADE IN REAL ANALYSIS 1

# Importing primary dependencies
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns

plt.style.use('ggplot')
# Importing Plotly and setting it as the default plotting library
import plotly.express as px
# Setting Plotly as the default plotting library for pandas
pd.options.plotting.backend = "plotly"

df.head()

df.info()

from datetime import datetime as dt

df[] = (dt.today() - df.dob).dt.days//365

px.bar(df.sort_values(['grade']), x = 'grade', color = 'gender')

px.histogram(df, x = 'tutorials', color = 'grade')

fig = px.scatter(df, x='tutorials', color='gender', color_discrete_sequence=['orange', 'green'])
fig.update_traces(marker=dict(size=12))
fig.update_layout(template='plotly_dark')
fig

fig = px.bar(df.sort_values(['grade']), x = 'grade', color = 'higher', color_discrete_sequence=['white','green'])
fig.update_layout(template='plotly_dark')
fig

fig = px.bar(df.sort_values(['grade']), x = 'grade', color = 'understood_RA')
fig.update_layout(template='plotly_dark')
fig

fig = px.bar(df.sort_values(['grade']), x = 'grade', color = 'prerequisite')
fig.update_layout(template='plotly_dark')
fig

dataset = df.iloc[:, 2:]

dataset.head()