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Today, one of the subscriber asked me to share one real life example from any of the random ML project. So let's discuss that 😄 Let's consider a simple real-life machine learning project: predicting house prices based on features such as location, size, and number of bedrooms. We'll use a dataset, train a model, and then use it to make predictions. ### Steps: 1. Data Collection: We'll use a publicly available dataset from Kaggle or any other source. 2. Data Preprocessing: Cleaning the data, handling missing values, and feature engineering. 3. Model Selection: Choosing a machine learning algorithm (e.g., Linear Regression). 4. Model Training: Training the model with the dataset. 5. Model Evaluation: Evaluating the model's performance using metrics like Mean Absolute Error (MAE). 6. Prediction: Using the trained model to predict house prices. I'll provide a simplified version of these steps. Let's assume we have the data available in a CSV file. ### Example with Python Code Step 1: Data Collection Let's assume we have a dataset named house_prices.csv. Step 2: Data Preprocessing

import pandas as pd

# Load the dataset
data = pd.read_csv('/mnt/data/house_prices.csv')

# Display the first few rows
data.head()

Step 3: Model Selection and Preprocessing

from sklearn.model_selection import train_test_split
from sklearn.linear_model import LinearRegression
from sklearn.metrics import mean_absolute_error

# Selecting relevant features
features = ['location', 'size', 'bedrooms']
target = 'price'

# Convert categorical variables to dummy variables
data = pd.get_dummies(data, columns=['location'], drop_first=True)

# Splitting the dataset into training and testing sets
X = data[features]
y = data[target]

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

# Initialize the model
model = LinearRegression()

Step 4: Model Training

# Train the model
model.fit(X_train, y_train)

Step 5: Model Evaluation

# Predict on the test set
y_pred = model.predict(X_test)

# Calculate the Mean Absolute Error
mae = mean_absolute_error(y_test, y_pred)
print(f'Mean Absolute Error: {mae}')

Step 6: Prediction

# Predict the price of a new house
new_house = pd.DataFrame({
    'location': ['LocationA'],
    'size': [2500],
    'bedrooms': [4]
})

# Convert categorical variables to dummy variables
new_house = pd.get_dummies(new_house, columns=['location'], drop_first=True)

# Ensure the new data has the same number of features as the training data
new_house = new_house.reindex(columns=X.columns, fill_value=0)

# Predict the price
predicted_price = model.predict(new_house)
print(f'Predicted House Price: {predicted_price[0]}')

This example outlines the entire process, from loading the data to making predictions with a trained model. You can adapt this example to more complex datasets and models based on your specific needs. Best Data Science & Machine Learning ENJOY LEARNING 👍👍

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Python script for creating an audiobook, suitable for a Telegram post: --- 🔊 Create an Audiobook with Python! 📚🎧 Transform your PDFs into audiobooks with this simple Python script using PyPDF2 and pyttsx3. Follow these steps to convert your PDF files into spoken words: 1. Install Required Libraries:

   pip install PyPDF2
   pip install pyttsx3
   

2. Python Script:

   import PyPDF2
   import pyttsx3

   # Read the PDF by specifying the path on your computer
   pdfReader = PyPDF2.PdfFileReader(open('clcoding.pdf', 'rb'))

   # Get the handle to speaker
   speaker = pyttsx3.init()

   # Split the pages and read one by one
   for page_num in range(pdfReader.numPages):
       text = pdfReader.getPage(page_num).extractText()
       speaker.say(text)
       speaker.runAndWait()

   # Stop the speaker after completion
   speaker.stop()

   # Save the audiobook at the specified path
   speaker.save_to_file(text, 'E:\\audio.mp3')
   speaker.runAndWait()
   

3. Enjoy Your Audiobook! This script reads your PDF and saves the audio as an MP3 file, making it easy to listen to your favorite documents on the go. Share your experiences and improvements in the comments! Happy coding! 🚀 ---

Python code To download from Youtube ⚙ from pytube import YouTube # Enter the YouTube video URL url = "https://www.youtube.com/watch?v=dQw4w9WgXcQ" # Create a YouTube object with the URL yt = YouTube(url) # Select the highest resolution video video = yt.streams.get_highest_resolution() # Set the output directory and filename output_dir = "/storage/emulated/0/Documents/" filename = yt.title+".mp4" # Download the video video.download(output_dir, filename) print(f"Download complete: {filename}")

Python code To download from Youtube ⚙ from pytube import YouTube # Enter the YouTube video URL url = "https://www.youtube.com/watch?v=dQw4w9WgXcQ" # Create a YouTube object with the URL yt = YouTube(url) # Select the highest resolution video video = yt.streams.get_highest_resolution() # Set the output directory and filename output_dir = "/storage/emulated/0/Documents/" filename = yt.title+".mp4" # Download the video video.download(output_dir, filename) print(f"Download complete: {filename}")

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