Do the Data Science Project A to Z With ChatGPT

Do full Data Science Project with ChatGPT: Guide to Machine Learning using Python, Data Visualization, and Web Scraping.

Introduction

In this article, I will do Classification Data science project from A to Z with ChatGPT to prove you that ChatGPT is really capable of doing Data Science project.

If you are a beginner and know more about Classification, here I explained the Classification terms A to Z.

Also, if you want to learn more, how ChatGPT can help you mastering Data Science, here, you can do this easily by copying what I did.

Now, as we already know, Data science is a mixture of many different disciplines, starting with Web Scraping to building Machine learning model to find meaning in the data.

That’s why I am splitting this article into three sections, which we will cover each of them;

• Finding a Dataset ( Web Scraping)
• Data Exploration
• Data Visualization
• Building Model ( Machine Learning)

We will do these by asking questions ChatGPT.

And, yes I have too many past articles about them, that’s why I add links if you want to more about these.

Let’s start with finding a dataset, that we will work on.

Finding a Dataset

Now, let’s find a dataset by asking ChatGPT. But here, we should be specific.

For the sake of this article, I wanted to apply classification tasks and also I want to find a dataset that I can reach out without doing much operation. 

If you want to do web scraping with ChatGPT, read this one.

Also, read this one for finding available datasets in the web.

Now, let’s ask ChatGPT.

Question

Hey chat, I want to do Data Science Project about classification. Can you offer me a distinct public dataset, for example from UCI Machine Learning Repository, that I can apply classification to it. Also please give me the link.

ChatGPT

Sure, I can recommend a few datasets from the UCI Machine Learning Repository that are suitable for classification tasks:

• Iris Dataset: This is a popular dataset for classification tasks, and it contains measurements of different iris flowers. The goal is to predict the species of iris based on the measurements. You can find the dataset at this link: https://archive.ics.uci.edu/ml/datasets/iris
• Wine Dataset: This dataset contains information about different wines, including their chemical composition and geographical origin. The goal is to predict the origin of the wine based on its characteristics. You can find the dataset at this link: https://archive.ics.uci.edu/ml/datasets/wine
• Breast Cancer Wisconsin (Diagnostic) Dataset: This dataset contains measurements of breast cancer tumors, and the goal is to predict whether a tumor is malignant or benign. You can find the dataset at this link: https://archive.ics.uci.edu/ml/datasets/Breast+Cancer+Wisconsin+(Diagnostic)
• Heart Disease Dataset: This dataset contains information about patients with heart disease, and the goal is to predict whether a patient has heart disease or not based on their characteristics. You can find the dataset at this link: https://archive.ics.uci.edu/ml/datasets/Heart+Disease

I choose breast cancer Wisconsin diagnostic dataset. Because Iris and Wine datasets were commonly used by other data scientist and I also did heart disease dataset, here.

Also, if you want to learn more about Classification, here you can have a general idea by reading this.

If you want master Data Science, follow me by doing same actions, and open your Python compiler to make most of this article.

Okey, I download this dataset, it is time to read this in my environment.

Question 2

Alright, I download this dataset, can you write to me a code to read this to my environment?

ChatGPT

Sure, I can help you with that. Assuming that you have downloaded the dataset in CSV format, you can use the Pandas library to read the dataset into your environment.

Here’s an example code snippet that shows how to read the Breast Cancer Wisconsin (Diagnostic) dataset into a Pandas DataFrame:

import pandas as pd

# Replace the file path with the path to the downloaded CSV file on your computer
file_path = 'path/to/breast-cancer-wisconsin-data.csv'

# Read the CSV file into a Pandas DataFrame
data = pd.read_csv(file_path)

# Print the first few rows of the DataFrame to verify that the data was loaded correctly
print(data.head())

Make sure to replace 'path/to/breast-cancer-wisconsin-data.csv' with the actual file path on your computer. Once you run this code, you should see the first few rows of the dataset printed to your console. From there, you can start working with the data and building your classification model.

Okay, as ChatGPT said to you, change the file_path to the actual file path of the dataset that you earlier download.

By the way, you can download the same dataset here too.

About the Dataset

The dataset contains features that are computed from digitized images of fine needle aspirates (FNAs) of breast masses, which describe characteristics of the cell nuclei present in the image. 

The features consist of ten real-valued measurements for each cell nucleus, such as radius, texture, perimeter, and symmetry, among others. The dataset also includes the diagnosis (M = malignant, B = benign) and ID number for each sample.

 The mean, standard error, and largest value of each feature were computed for each image, resulting in a total of 30 features.

For more information, here.

Data Exploration

Wonderful. Data Exploration is a stage that you will be familiar with the Data you are working on. I do not want to say more, let’s leave this job to our AI assistant, ChatGPT.

Yet before doing this, I want you to run the following codes and we will use the outputs.

data.head()

data.info()

Okey now lets get into the question;

Question 3

Okey here is the sample of my data.

“Here is the output of the first line(df.head))”

Here is the info of my data;

“Here is the output of the second line(df.info())”

Can you write to me 5 different codes which will help to explore this data?

ChatGPT

Certainly! Here are 5 different code snippets that will help you explore the breast cancer Wisconsin dataset:

• To get a summary of the dataset, including information about the number of rows and columns, the data types, and the number of non-null values in each column, you can use the info() method:

data.info()

2. To get basic descriptive statistics about the numeric columns in the dataset, you can use the describe() method:

data.describe()

3. To count the number of occurrences of each unique value in the diagnosis column, you can use the value_counts() method:

data['diagnosis'].value_counts()

4. To plot a histogram of the radius_mean column, you can use the hist() method:

import matplotlib.pyplot as plt

data['radius_mean'].hist()
plt.xlabel('Radius Mean')
plt.ylabel('Frequency')
plt.title('Histogram of Radius Mean')
plt.show()

5. To plot a scatter plot of the radius_mean column against the texture_mean column, with different colors for the benign and malignant diagnoses, you can use the scatter() method:

colors = {'M': 'red', 'B': 'blue'}
plt.scatter(data['radius_mean'], data['texture_mean'], c=data['diagnosis'].apply(lambda x: colors[x]))
plt.xlabel('Radius Mean')
plt.ylabel('Texture Mean')
plt.title('Scatter Plot of Radius Mean vs. Texture Mean')
plt.show()

I hope these examples help you get started with exploring your data! Let me know if you have any questions.

Great now let’s skip data visualization section.

Data Visualization

Now, it is time to make some data visualization. It will help you to detect the patterns in your Data. 

That will give you better insights about your data.

If you involved in Data Visualization in your data project and you need help, here is the article that I will deeply discover, how ChatGPT will help you do data visualization.

Now, let’s again ask to our AI assistant to help make data visualization for our project.

Question 4

Okey, now I will build machine learning model to predict the classification of Diagnosis, which is M = malignant, B = benign. I want to do data visualization to detect patterns and gather meaningful information from it. Can you offer me the application of 5 different graphs in Python with codes?

ChatGPT

• Histogram: A histogram is a graphical representation of the distribution of a dataset. It can help you visualize the spread of your data and identify any outliers or gaps.

import matplotlib.pyplot as plt
import pandas as pd

# Load the dataset
df = pd.read_csv('breast_cancer_data.csv')

# Plot a histogram of the radius mean for benign and malignant tumors
plt.hist(df[df['diagnosis'] == 'M']['radius_mean'], alpha=0.5, label='Malignant')
plt.hist(df[df['diagnosis'] == 'B']['radius_mean'], alpha=0.5, label='Benign')
plt.legend(loc='upper right')
plt.title('Distribution of Radius Mean by Diagnosis')
plt.xlabel('Radius Mean')
plt.ylabel('Frequency')
plt.show()

2. Scatter plot: A scatter plot is a diagram where each value in the dataset is represented by a dot. It can help you visualize the relationship between two variables.

import matplotlib.pyplot as plt
import pandas as pd

# Load the dataset
df = pd.read_csv('breast_cancer_data.csv')

# Plot a scatter plot of radius mean vs. texture mean, colored by diagnosis
colors = {'M': 'red', 'B': 'blue'}
plt.scatter(df['radius_mean'], df['texture_mean'], c=df['diagnosis'].map(colors))
plt.title('Radius Mean vs. Texture Mean by Diagnosis')
plt.xlabel('Radius Mean')
plt.ylabel('Texture Mean')
plt.show()

3. Box plot: A box plot is a graphical representation of the distribution of a dataset through their quartiles. It can help you visualize the spread and skewness of your data.

import matplotlib.pyplot as plt
import pandas as pd

# Load the dataset
df = pd.read_csv('breast_cancer_data.csv')

# Plot a box plot of the worst concavity by diagnosis
plt.boxplot([df[df['diagnosis'] == 'M']['concavity_worst'], df[df['diagnosis'] == 'B']['concavity_worst']],
            labels=['Malignant', 'Benign'])
plt.title('Worst Concavity by Diagnosis')
plt.xlabel('Diagnosis')
plt.ylabel('Worst Concavity')
plt.show()

4. Bar chart: A bar chart is a graphical representation of categorical data with rectangular bars. It can help you visualize the distribution of a dataset by categories.

import matplotlib.pyplot as plt
import pandas as pd

# Load the dataset
df = pd.read_csv('breast_cancer_data.csv')

# Compute the number of benign and malignant tumors by diagnosis
counts = df['diagnosis'].value_counts()

# Plot a bar chart of the number of benign and malignant tumors by diagnosis
plt.bar(['Benign', 'Malignant'], counts.values)
plt.title('Number of Tumors by Diagnosis')
plt.xlabel('Diagnosis')
plt.ylabel('Number of Tumors')
plt.show()

5. Heatmap: A heatmap is a graphical representation of data where values are represented as colors. It can help you visualize the relationship between variables in a dataset.

import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns

# Load the dataset
df = pd.read_csv('breast_cancer_data.csv')

# Compute the correlation matrix
corr = df.corr()

# Plot a heatmap of the correlation matrix
sns.heatmap(corr, annot=True)
plt.title('Correlation Matrix')
plt.show()

Alright, since we already did the Data Visualization too, it is time to build our mode.

Building Machine Learning Model

Alright, we will use classification algorithms. There are many different machine learning algorithms, which one to pick?

According to my experiences, it will be better to apply more than 1 classification algorithm, and compare the result and then choose the best-performanced model for the sake of efficiency.

And again, if you want to learn more how to get help from ChatGPT about machine learning, read this.

Alright, let’s ask ChatGPT again.

Question 5

Hi Chat, I want you to help me develop 6 Classification algorithms by using Sci-kit learn library to predict of Diagnosis, which is M = malignant, B = benign. You already know my dataset and it’s features, the feature that I want to predict is “diagnosis”. Can you write to me the code of this, also I want to assign the evaluation metrics to the prediction data frame to compare afterwards. Also please use precision, recall and f1 scores as an evaluation metrics.

ChatGPT

Sure! Here is an example code that implements six classification algorithms using scikit-learn library to predict the “diagnosis” feature in the breast cancer dataset:

import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.tree import DecisionTreeClassifier
from sklearn.ensemble import RandomForestClassifier
from sklearn.neighbors import KNeighborsClassifier
from sklearn.naive_bayes import GaussianNB
from sklearn.linear_model import LogisticRegression
from sklearn.svm import SVC
from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score

# Load the dataset
df = pd.read_csv('breast_cancer_data.csv')

# Prepare the dataset
X = df.drop(['id', 'diagnosis', 'Unnamed: 32'], axis=1)
y = df['diagnosis']
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

# Initialize the classifiers
dt = DecisionTreeClassifier()
rf = RandomForestClassifier()
knn = KNeighborsClassifier()
nb = GaussianNB()
lr = LogisticRegression()
svm = SVC()

# Train the classifiers
dt.fit(X_train, y_train)
rf.fit(X_train, y_train)
knn.fit(X_train, y_train)
nb.fit(X_train, y_train)
lr.fit(X_train, y_train)
svm.fit(X_train, y_train)

# Predict using the classifiers
dt_pred = dt.predict(X_test)
rf_pred = rf.predict(X_test)
knn_pred = knn.predict(X_test)
nb_pred = nb.predict(X_test)
lr_pred = lr.predict(X_test)
svm_pred = svm.predict(X_test)

# Evaluate the classifiers
dt_scores = [accuracy_score(y_test, dt_pred), precision_score(y_test, dt_pred, pos_label='M'),
             recall_score(y_test, dt_pred, pos_label='M'), f1_score(y_test, dt_pred, pos_label='M')]
rf_scores = [accuracy_score(y_test, rf_pred), precision_score(y_test, rf_pred, pos_label='M'),
             recall_score(y_test, rf_pred, pos_label='M'), f1_score(y_test, rf_pred, pos_label='M')]
knn_scores = [accuracy_score(y_test, knn_pred), precision_score(y_test, knn_pred, pos_label='M'),
              recall_score(y_test, knn_pred, pos_label='M'), f1_score(y_test, knn_pred, pos_label='M')]
nb_scores = [accuracy_score(y_test, nb_pred), precision_score(y_test, nb_pred, pos_label='M'),
             recall_score(y_test, nb_pred, pos_label='M'), f1_score(y_test, nb_pred, pos_label='M')]
lr_scores = [accuracy_score(y_test, lr_pred), precision_score(y_test, lr_pred, pos_label='M'),
             recall_score(y_test, lr_pred, pos_label='M'), f1_score(y_test, lr_pred, pos_label='M')]
svm_scores = [accuracy_score(y_test, svm_pred), precision_score(y_test, svm_pred, pos_label='M'),
              recall_score(y_test, svm_pred, pos_label='M'), f1_score(y_test, svm_pred, pos_label='M')]

# Create the evaluation dataframe
eval_df = pd.DataFrame({'Decision Tree': dt_scores, 'Random Forest': rf_scores, 'KNN': knn_scores,
                        'Naive Bayes': nb_scores, 'Logistic Regression': lr_scores, 'SVM': svm_scores},
                       index=['Accuracy', 'Precision', 'Recall', 'F1 Score'])
print(eval_df)

Wonderful!

Do not forget removing here to this code;

# Load the dataset
df = pd.read_csv('breast_cancer_data.csv')

Because we already did that action.

And also if you do not know what precision, recall and f1 score, here is the article that I explained to you the terms of classification.

Also, the results are amazing, here is the evaluation data frame.

Of course, the result might vary in your trial, but that wont make these score worst.

Conclusion

In this article, we together discovered how ChatGPT can help you doing your Data Science project in multiple sections like finding related data, exploring it, making well informed visuals and applying different models on it.

Of course, this is not best way of doing data science project, yet I assure you, if you are not familiar of using AI tools in your project, some one might do the same job with you in lesser time.

I am not a big fan of saying that AI will take our jobs because I am well aware that it can make a lot of mistakes that human can find out easily. Yet in many field it is really useful assistant to save time and increase the efficiency.

Thanks for reading.

Here is my Numpy cheat sheet.

Here is the source code of the “How to be a Billionaire” data project.

Here is the source code of the “Classification Task with 6 Different Algorithms using Python” data project.

Here is the source code of the “Decision Tree in Energy Efficiency Analysis” data project.