Python-for-Machine-Learning/C3/Random-Forest/English - Revision history

Madhurig at 14:18, 17 July 2025

2025-07-17T14:18:31Z

Madhurig at 10:08, 16 July 2025

2025-07-16T10:08:28Z

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Nirmala Venkat: Created page with "
{| border="1" |- || '''Visual Cue''' || '''Narration''' |- ||
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2025-07-04T10:20:18Z

Created page with " <div style="margin-left:1.27cm;margin-right:0cm;"></div> {| border="1" |- || '''Visual Cue''' || '''Narration''' |- || <div style="color:#000000;">Show slide:</div> <div s..."

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{| border="1"
|-
|| '''Visual Cue'''
|| '''Narration'''
|-
|| <div style="color:#000000;">Show slide:</div>

<div style="color:#000000;">'''Welcome'''</div>

|| <span style="color:#000000;">Welcome to the Spoken Tutorial on</span><span style="color:#000000;">''' Random Forest'''</span><span style="color:#000000;">.</span>
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|| <div style="color:#000000;">Show Slide:</div>

<div style="color:#000000;">'''Learning Objectives'''</div>

|| In this tutorial, we will learn about
* <div style="margin-left:1.27cm;margin-right:0cm;">'''Ensemble Learning and'''</div>
* <div style="margin-left:1.27cm;margin-right:0cm;">'''Random Forest'''</div>

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|| <span style="color:#000000;">Show Slide:</span>

|| To Record this tutorial, I am using
* <div style="margin-left:1.27cm;margin-right:0cm;"><span style="color:#000000;">'''Ubuntu Linux operating system 2</span>4<span style="color:#000000;">.04'''</span></div>
* <div style="margin-left:1.27cm;margin-right:0cm;"><span style="color:#000000;">'''Jupyter Notebook'''</span><span style="color:#000000;"> </span><span style="color:#000000;">'''IDE'''</span></div>

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|| <div style="color:#000000;">Show Slide:</div>

<span style="color:#000000;">'''Prerequisite'''</span>
|| To follow this tutorial,
* <div style="margin-left:1.27cm;margin-right:0cm;"><span style="color:#000000;">The learner must have basic </span>knowledge of<span style="color:#000000;"> </span>'''P<span style="color:#000000;">ython</span>.'''</div>
* <div style="margin-left:1.27cm;margin-right:0cm;"><span style="color:#000000;">For pre-requisite </span><span style="color:#000000;">'''Python'''</span><span style="color:#000000;"> tutorials, please visit this website.</span></div>

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|| <div style="color:#000000;">Show Slide:</div>

<div style="color:#000000;">'''Code files'''</div>
||
* <div style="margin-left:1.27cm;margin-right:0cm;">The files used in this tutorial are provided in the '''Code files '''link.</div>
* <div style="color:#252525;margin-left:1.27cm;margin-right:0cm;">Please download and extract the files.</div>
* <div style="color:#252525;margin-left:1.27cm;margin-right:0cm;">Make a copy and then use them while practicing.</div>

|- style="border:0.5pt solid #000000;padding-top:0cm;padding-bottom:0cm;padding-left:0.199cm;padding-right:0.191cm;"
|| Show slide:

'''Ensemble Learning'''
||
* <div style="margin-left:1.27cm;margin-right:0cm;"><span style="color:#000000;">'''Ensemble learning'''</span><span style="color:#000000;"> combines multiple models to improve performance.</span></div>
* <div style="color:#000000;margin-left:1.27cm;margin-right:0cm;">It reduces errors by averaging predictions from different models.</div>
* <div style="margin-left:1.27cm;margin-right:0cm;"><span style="color:#000000;">One popular ensemble method is </span><span style="color:#000000;">'''Random Forest'''</span><span style="color:#000000;">, which uses decision trees.</span></div>

|- style="border:0.5pt solid #000000;padding-top:0cm;padding-bottom:0cm;padding-left:0.199cm;padding-right:0.191cm;"
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'''Random Forest'''
||
* <div style="margin-left:1.27cm;margin-right:0cm;"><span style="color:#000000;">'''Random Forest'''</span><span style="color:#000000;"> builds </span><span style="color:#000000;">'''multiple trees'''</span><span style="color:#000000;"> and takes the majority vote.</span></div>
* <div style="color:#000000;margin-left:1.27cm;margin-right:0cm;">This improves accuracy and reduces overfitting compared to a single tree.</div>
* <div style="margin-left:1.27cm;margin-right:0cm;"><span style="color:#000000;">It is widely used for </span><span style="color:#000000;">'''classification'''</span><span style="color:#000000;"> and </span><span style="color:#000000;">'''regression'''</span><span style="color:#000000;"> tasks.</span></div>

|- style="border:0.5pt solid #000000;padding-top:0cm;padding-bottom:0cm;padding-left:0.199cm;padding-right:0.191cm;"
|| Point to the''' RandomForest.ipynb'''
||

'''RandomForest'''.'''ipynb''' is the python notebook file for the demonstration of Random Forest

|- style="border:0.5pt solid #000000;padding-top:0cm;padding-bottom:0cm;padding-left:0.199cm;padding-right:0.191cm;"
|| Press '''Ctrl,Alt and T''' keys

Type '''conda activate ml'''

Press '''Enter'''
|| Let us open the Linux terminal. Press '''Ctrl, Alt''' and '''T '''keys together.

Activate the machine learning environment by typing

'''conda space activate''' '''space ml'''

Press '''Enter.'''
|- style="border:0.5pt solid #000000;padding-top:0cm;padding-bottom:0cm;padding-left:0.199cm;padding-right:0.191cm;"
|| Go to the '''Downloads '''folder

Type '''cd Downloads'''
Press '''Enter'''
Type '''jupyter notebook'''
Press '''Enter'''
|| I have saved my code file in the '''Downloads '''folder.

Please navigate to the respective folder of your code file location.

Then type, '''jupyter space notebook '''and press Enter to open Jupyter Notebook.
|- style="border:0.5pt solid #000000;padding-top:0cm;padding-bottom:0cm;padding-left:0.199cm;padding-right:0.191cm;"
|| Show '''Jupyter Notebook Home page''':

Click on '''RandomForest.ipynb''' file
|| We can see the '''Jupyter Notebook''' '''Home page''' has opened in the web browser.

Click the''' RandomForest dot ipynb''' file to open it.

Note that each cell will have the output displayed in this file.
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|| Highlight the lines:

'''import pandas as pd'''
'''import numpy as np'''
'''from sklearn.metrics import accuracy_score'''
|| We start by importing the required libraries for the Random Forest model.

Please remember to Execute each cell by pressing '''Shift and Enter''' to get output.

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'''california = fetch_california_housing()   '''

'''housing = pd.DataFrame(california.data, columns=california.feature_names) '''

'''housing['MedHouseVal'] = california.target '''
|| We use the '''California Housing dataset''' from '''sklearn library.'''

It has housing data from California districts.

We analyze the dataset and predict the '''MedHouseValue'''.

Now, we load the California Housing dataset into a Pandas DataFrame.

We add the target column, '''MedHouseVal,''' which represents median house value.
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'''housing.head() '''
|| To check the dataset, we display the first few rows using the '''head function'''.
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'''housing.shape '''

|| Next, we use the '''shape function''' to check the number of rows and columns.
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'''housing.info()'''

|| Now, we print '''dataset details''' to understand its '''structure''' and '''statistics.'''
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'''housing_sorted = housing.sort_values(by="HouseAge")'''

|| Let's visualize '''house age vs median value''' using a '''line plot'''.
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|| The plot shows newer homes have higher values, then prices stabilize.

The shaded region represents '''price variability''' in different age groups.
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'''skewed_features = ['AveRooms', 'AveBedrms', 'Population', 'AveOccup'] '''

|| Before training, we check for '''skewed features''' that may affect predictions.

Next, we apply '''log transformation''' to reduce '''skewness''' in these features.
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'''scaler = StandardScaler()'''

|| Now, we scale the feature values using '''StandardScaler'''.

This ensures all features have a similar range for better modeling.
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'''x = housing.drop(columns=["MedHouseVal"])'''

'''x.head() '''
|| Then we separate the '''features''' by dropping the target column.

After that, we display the first few rows to check the data.
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'''y = housing.MedHouseVal'''
'''y.head() '''

|| Next, we extract the '''target variable''' and store it in '''y.'''
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'''x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.3, random_state=42) '''
|| Now, we split the dataset into '''training''' and '''testing sets.'''
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'''rf ='''
|| Next, we create a '''Random Forest Regressor''' with '''100 trees.'''

Then, we train the model using the training data.
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'''y_train_pred = rf.predict(x_train) '''

|| After training, we predict house values for the training set.
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'''training_mse ='''
|| Now, we compute the '''Mean Squared Error''' for training data.

We also compute the '''adjusted R squared score''' for better evaluation.

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|| The '''Training MSE '''is '''0.037''', showing a low prediction error.

The '''Training Adjusted R squared Score''' is '''0.972'''.A high Adjusted R squared score suggests the model explains most variance.
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'''y_pred = rf.predict(x_test)'''
|| Now, we predict house prices for the test dataset.
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'''print("Random Forest - Actual vs Predicted:") '''
|| Next, we compare actual vs predicted values for the test set.
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'''test_mse = mean_squared_error(y_test,y_pred)'''

|| After that, we calculate the '''MSE''' for the test predictions.

Then, we compute the '''Adjusted R squared score''' for the test set.
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|| For the test set, the '''MSE''' is '''0.257''', indicating reasonable accuracy'''.'''

The '''Test Adjusted R squared Score''' is '''0.804'''.

A higher Adjusted R Squared score indicates a good fit.
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'''residuals = y_test - y_pred'''
'''plt.show()'''

|| To further analyze performance, we examine the '''residuals'''.
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|| Residuals are the difference between '''actual and predicted values.'''

The red dashed line marks zero residuals for reference.

Most residuals are near zero, meaning the prediction error is low.
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'''feature_importances = rf.feature_importances_'''

'''plt.show()'''
|| Besides accuracy, we analyze '''feature importance''' to see key prediction factors.

Feature importance shows how much each feature impacts predictions.

Higher value mean a feature is more important for the model.
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|| Show the output:
|| The plot shows '''MedInc''' has the highest impact on house price predictions.

Other features contribute less but still affect model performance.
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|| Narration
|| Thus, we successfully built a '''Random Forest model''' for house price prediction.

The model showed '''high accuracy''', indicating '''strong performance.'''
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|| Show slide:

'''Summary'''
|| This brings us to the end of the tutorial. Let us summarize.

In this tutorial, we have learnt about
* <div style="margin-left:1.27cm;margin-right:0cm;">'''Ensemble Learning and'''</div>
* <div style="color:#000000;margin-left:1.27cm;margin-right:0cm;">'''Random Forest'''</div>

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|| Show Slide:

'''Assignment'''
|| As an assignment, please do the following:

* <div style="margin-left:1.27cm;margin-right:0cm;">Replace the '''test_size parameter''' as shown here.</div>
* <div style="margin-left:1.27cm;margin-right:0cm;">Observe the change in '''MSE '''and '''Adjusted R squared score'''</div>

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|| Show Slide:

'''Assignment Solution'''

'''Show x img'''
|| After completing the assignment, the output should match as the expected result.
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'''FOSSEE Forum'''
|| For any general or technical questions on '''Python for Machine Learning''', visit the '''FOSSEE forum''' and post your question.

|- style="border:0.5pt solid #000000;padding-top:0cm;padding-bottom:0cm;padding-left:0.199cm;padding-right:0.191cm;"
|| Show Slide:

'''Thank you'''

|| This is''' Harini Theiveegan, '''a FOSSEE Summer Fellow 2025, IIT Bombay signing off.

Thanks for joining
|-
|}