Create your Python scripts Using ChatGPT.

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To write Python scripts, Here is a chatGPT  prompt template

Develop a Python script that generates [enter your idea].

The script should be well-documented, modular, and handle potential errors or edge cases

 

Description

To write Python scripts Using ChatGPT, Here is a chatGPT  prompt template

Develop a Python script that generates [enter your idea].

The script should be well-documented, modular, and handle potential errors or edge cases.

Sample Prompt:

Develop a Python script that generates  House Price Prediction

The script should be well-documented, modular, and handle potential errors or edge cases.

Sample Response:

Here’s a Python script for House Price Prediction. This script is designed to use a machine learning model to predict the price of a house based on a set of input features.

import pandas as pd
import numpy as np
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LinearRegression

# Load the data into a pandas dataframe
data = pd.read_csv(‘house_prices.csv’)

# Define the input features (X) and the target variable (y)
X = data.drop(‘price’, axis=1)
y = data[‘price’]

# Split the data into training and testing sets
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

# Fit a linear regression model to the training data
model = LinearRegression()
model.fit(X_train, y_train)

# Predict the prices of the houses in the test set
y_pred = model.predict(X_test)

# Calculate the root mean squared error (RMSE) of the model
rmse = np.sqrt(np.mean((y_pred – y_test)**2))

print(f”Root Mean Squared Error: {rmse}”)

This script assumes that you have a dataset of house prices stored in a CSV file called house_prices.csv. The script loads the data into a pandas data frame, separates the input features (X) and target variable (y), splits the data into training and testing sets, fits a linear regression model to the training data, and then predicts the prices of the houses in the test set. Finally, it calculates the root mean squared error of the model and prints it to the console.

To run this script, simply save it as a .py file and run it in your terminal or IDE. Note that you may need to install the necessary libraries (pandas, numpy, and scikit-learn) if you haven’t done so already. Also, make sure that your dataset is in the correct format and that the file path is correct.