Upload 2247_159_252 (1).py

2247_159_252 (1).py

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+# -*- coding: utf-8 -*-
+"""2247.159.252
+
+Automatically generated by Colab.
+
+Original file is located at
+    https://colab.research.google.com/drive/1zzXHR2L7JfiEtViUgooXP5iYBIeu2gkf
+"""
+
+import warnings
+import numpy as np
+import pandas as pd
+import plotly.express as px
+from sklearn.linear_model import LinearRegression
+from sklearn.model_selection import train_test_split
+from sklearn.metrics import r2_score, mean_absolute_error, mean_squared_error
+warnings.filterwarnings('ignore')
+
+df = pd.read_csv('/content/Wprld population growth rate by cities 2024.csv')
+
+df.info()
+
+df.describe().T
+
+df.head()
+
+df.isnull().sum().sort_values(ascending=False)
+
+df['Continent'].unique()
+
+df[df['Continent'].isnull()]
+
+df.duplicated().any()
+
+df.loc[df['Continent'] == 'Oceana', 'Continent'] = 'Oceania'
+
+con = ['North America', 'Africa', 'Europe', 'Africa', 'Europe', 'Africa', 'Europe', 'Africa', 'Europe', 'Europe', 'Europe']
+ind = df[df['Continent'].isnull()].index
+
+df['Count'] = 1
+
+fig = px.pie(df, names='Continent', values='Count')
+fig.update_layout(legend_title='Continent', title={'text': 'Distribution of Continents', 'y':0.95, 'x':0.5, 'xanchor': 'center', 'yanchor': 'top'})
+fig.show()
+
+for c in df['Continent'].unique():
+  fig = px.histogram(df[df['Continent'] == c], x='Count', y='Country', color='Country', color_discrete_sequence=px.colors.qualitative.Dark24).update_yaxes(categoryorder='total ascending')
+  fig.update_layout(title={'text': f'Distribution of Country in {c}', 'y':0.95, 'x':0.5, 'xanchor': 'center', 'yanchor': 'top'}, xaxis_title='Sum of Count')
+  fig.show()
+
+for n in ['Population (2023)', 'Population (2024)', 'Growth Rate']:
+  fig = px.histogram(df, x=n, y="Count",
+                     marginal="box",
+                     hover_data=df.columns)
+  fig.update_layout(title={'text': f'Distribution of {n}', 'y':0.95, 'x':0.5, 'xanchor': 'center', 'yanchor': 'top'}, yaxis_title='Sum of Count')
+  fig.show()
+
+country = df.groupby('Country', as_index=False)[['Growth Rate']].mean()
+city = df.groupby('City', as_index=False).agg({'Population (2024)': 'sum', 'Growth Rate': 'mean'})
+
+fig = px.box(df, x='Continent', y='Growth Rate', color='Continent', color_discrete_sequence=px.colors.qualitative.Dark24)
+fig.update_layout(title={'text': 'Growth Rate by Continent','y':0.95,'x':0.5,'xanchor': 'center','yanchor': 'top'})
+fig.show()
+
+fig = px.bar(country.sort_values('Growth Rate', ascending=False)[:10], x='Country', y='Growth Rate', color='Country', color_discrete_sequence=px.colors.qualitative.Dark24)
+fig.update_layout(title={'text': 'Top 10 Countries with Highest Average Growth Rate', 'y':0.95, 'x':0.5, 'xanchor': 'center', 'yanchor': 'top'})
+fig.show()
+
+fig = px.bar(city.sort_values('Growth Rate', ascending=False)[:10], x='City', y='Growth Rate', color='City', color_discrete_sequence=px.colors.qualitative.Dark24)
+fig.update_layout(title={'text': 'Top 10 Cities with Highest Averate Growth Rate', 'y':0.95, 'x':0.5, 'xanchor': 'center', 'yanchor': 'top'})
+fig.show()
+
+fig = px.bar(country.sort_values('Growth Rate')[:10], x='Country', y='Growth Rate', color='Country', color_discrete_sequence=px.colors.qualitative.Dark24)
+fig.update_layout(title={'text': 'Top 10 Countries with Lowest Growth Rate', 'y':0.95, 'x':0.5, 'xanchor': 'center','yanchor': 'top'})
+fig.show()
+
+fig = px.bar(city.sort_values('Population (2024)', ascending=False)[:5], x='City', y='Population (2024)', color='City', color_discrete_sequence=px.colors.qualitative.Dark24)
+fig.update_layout(title={'text': 'Top 5 Cities with Highest Population (2024)', 'y':0.95, 'x':0.5, 'xanchor': 'center', 'yanchor': 'top'})
+fig.show()
+
+fig = px.bar(city.sort_values('Population (2024)') [:5], x='City', y='Population (2024)', color='City', color_discrete_sequence=px.colors.qualitative.Dark24)
+fig.update_layout(title={'text': 'Top 5 Cities with Lowest Population (2024)', 'y':0.95, 'x':0.5, 'xanchor': 'center', 'yanchor': 'top'})
+fig.show()
+
+df_num = df.drop(columns=['Count']).select_dtypes(include=np.number)
+fig = px.imshow(df_num.corr())
+fig.update_layout(title={'text': 'Correlation Between Numerical Attributes', 'y':0.95, 'x':0.5, 'xanchor': 'center', 'yanchor': 'top'})
+fig.show()
+
+X = df[['Population (2023)', 'Growth Rate']]
+y = df['Population (2024)']
+
+X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
+
+model = LinearRegression()
+model.fit(X_train, y_train)
+y_pred = model.predict(X_test)
+print(f'R2 Score: {round(r2_score(y_test, y_pred)*100,2)}%')
+print(f'Mean Absolute Error: {round(mean_absolute_error(y_test, y_pred), 2)}')
+print(f'Root Mean Squared Error: {round(np.sqrt(mean_squared_error(y_test, y_pred)), 2)}\n')