Many of us are not familiar with AI, but don’t worry. You have AI as your helper i.e. Artificial Intelligence like Gemini and ChatGPT etc. What do you not understand? Gemini and ChatGPT (ChatGPT vs Gemini) – Which one is the most beneficial assistant in your coding journey?
AI has become a popular companion in this modern era to make your activities go faster. We will discuss ChatGPT and Gemini AI in this blog.
Table of Contents
Summary of ChatGPT vs Gemini AI
| ChatGPT | Gemini | |
|---|---|---|
| the creator | OpenAI | |
| The model used | GPT-3.5 | Gemini Pro |
| Data sources | Massive text dataset (Sept. 2021 limit for Free tier), real-time web search (ChatGPT Plus)pen_spark | Likely web archives (Common Crawl), image-text databases, proprietary data; real-time web search capabilitiespen_spark |
| accepted language | You can use 9 different languages in ChatGPT including English, Chinese, French and Spanish. | You can use 40 different languages in Gemini, out of which you will get 9 different languages available in ChatGPT. |
| the price | free of charge | free of charge |
Why should you use AI Chatbot to write code?
There are several importance behind using AI Chat bot for writing code like –
- This Chatbot automatically advises you on repetitive coding tasks like writing boilerplate code, generating basic functions and code snippets. This will free you up a lot from thinking and will increase the automaticity of your brain.
- You can check your written code with AI Chatbot and through this, if there is any mistake in your code, you can easily check and fix it with the help of AI Chatbot and you can improve it later.
- If you don’t understand somewhere in the code, the AI Chatbot will easily explain it to you and may show you some simple method, through which you can easily complete the work of the code.
- AI Chatbot is adopting new coding methods and new languages that are available day by day. Through this, if you have taken the help of AI Chatbot for the language of your choice, you can also be connected with the code or language of advanced technology available today.
- If you are working on any international project then you can easily complete this project with AI Chatbot.
ChatGPT vs Gemini ⋮ Which is better for building a website?
In this section we will compare which of the two AI assistants ChatGPT and Gemini can help you code more. We will compare their strengths, who has a better understanding of coding, who can easily cover the various aspects of building a website and who can easily present complex content to us.
Prompt:
Basic HTML code for a word-counting website.
ChatGPT
<!DOCTYPE html> <html lang="en">
<head>
<meta charset="UTF-8">
<meta name="viewport" content="width=device-width, initial-scale=1.0">
<title>Word Count Tool</title>
<style>body {
font-family: Arial, sans-serif;
margin: 0;
padding: 20px;
}
textarea {
width: 100%;
height: 200px;
margin-bottom: 10px;
padding: 10px;
font-size: 16px;
}
button {
padding: 10px 20px;
font-size: 16px;
cursor: pointer;
}
#wordCount {
font-size: 18px;
margin-top: 10px;
}
</style></head>
<body>
<h1>Word Count Tool</h1>
<p>Type or paste your text below to count the number of words:</p>
<textarea id="textInput" placeholder="Enter your text here..."></textarea> <button onclick="countWords()">Count Words</button>
<p>ChatGPT vs Gemini</p>
<p>ChatGPT vs Gemini</p>
<div id="wordCount"></div><script>
function countWords() {
var text = document.getElementById('textInput').value;
var wordCount = text.split(/\s+/).filter(function(word) {
return word.length > 0;
}).length;
document.getElementById('wordCount').innerText = "Word count: " + wordCount;
}
</script></body>
</html>Output:
Gemini
<!DOCTYPE html>
<html lang="en">
<head>
<meta charset="UTF-8">
<title>Word Counter</title>
</head><body>
<h1>Word Counter</h1>
<textarea id="textInput" placeholder="Enter your text here..."></textarea>
<button onclick="countWords()">Count Words</button>
<p>ChatGPT vs Gemini</p>
<p>ChatGPT vs Gemini</p>
<p>ChatGPT vs Gemini</p>
<div id="wordCount">Word count: 0</div><script>
function countWords() {
// Get the text from the textarea
const text = document.getElementById("textInput").value;// Split the text into words (remove leading/trailing spaces)
const words = text.trim().split(/\s+/);
// Update the word count display
const wordCountDisplay = document.getElementById("wordCount");
wordCountDisplay.textContent = `Word count: ${words.length}`;
}
</script></body>
</html>Output:
Based on the final results it can be said that the code published by ChatGPT has given better results in designing a website with ChatGPT vs Gemini.
ChatGPT vs Gemini in writing Python code
Prompt: Write a fully documented Python code to load and read the breast cancer dataset from the sk-learn library, and then give me some insights from the data along with visualizations, and then use multiple algorithms and choose the one machine learning model to use on the dataset and give the results for the model as well.
ChatGPT
# Import necessary libraries
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn.datasets import load_breast_cancer
from sklearn.model_selection import train_test_split, cross_val_score
from sklearn.preprocessing import StandardScaler
from sklearn.metrics import accuracy_score, classification_report
from sklearn.linear_model import LogisticRegression
from sklearn.svm import SVC
from sklearn.ensemble import RandomForestClassifier
from sklearn.neighbors import KNeighborsClassifier
from sklearn.model_selection import GridSearchCV# Load the breast cancer dataset
data = load_breast_cancer()# Extract features and target
X = data.data
y = data.target# Create a DataFrame for better visualization
df = pd.DataFrame(data=np.c_[X, y], columns=np.append(data.feature_names, ['target']))
df['target'] = df['target'].astype(int)# 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)# Standardize features
scaler = StandardScaler()
X_train_scaled = scaler.fit_transform(X_train)
X_test_scaled = scaler.transform(X_test)# Insights and Visualizations
# 1. Distribution of target classes
plt.figure(figsize=(6, 4))
sns.countplot(data=df, x='target')
plt.title('Distribution of Target Classes')
plt.xlabel('Target')
plt.ylabel('Count')
plt.show()# 2. Correlation between features
plt.figure(figsize=(12, 8))
sns.heatmap(df.corr(), annot=True, cmap='coolwarm', fmt='.2f')
plt.title('Correlation Heatmap')
plt.show()# 3. Pairplot of a few important features
plt.figure(figsize=(12, 8))
sns.pairplot(df[['mean radius', 'mean texture', 'mean perimeter', 'mean area', 'target']], hue='target', diag_kind='kde')
plt.show()# Choose the best suitable machine learning model
# Models to be evaluated: Logistic Regression, Support Vector Machine, Random Forest, K-Nearest Neighbors
models = {
"Logistic Regression": LogisticRegression(max_iter=10000),
"Support Vector Machine": SVC(kernel='linear'),
"Random Forest": RandomForestClassifier(n_estimators=100),
"K-Nearest Neighbors": KNeighborsClassifier()
}# Evaluate models using cross-validation
results = {}
for name, model in models.items():
scores = cross_val_score(model, X_train_scaled, y_train, cv=5)
results[name] = scores.mean()# Print cross-validation results
print("Cross-validation results:")
for name, score in results.items():
print(f"{name}: {score:.4f}")# Select the best model
best_model_name = max(results, key=results.get)
best_model = models[best_model_name]
print("\nBest model:", best_model_name)# Train and evaluate the best model
best_model.fit(X_train_scaled, y_train)
y_pred_train = best_model.predict(X_train_scaled)
y_pred_test = best_model.predict(X_test_scaled)train_accuracy = accuracy_score(y_train, y_pred_train)
test_accuracy = accuracy_score(y_test, y_pred_test)print("\nTraining Accuracy:", train_accuracy)
print("Testing Accuracy:", test_accuracy)
print("\nClassification Report:")
print(classification_report(y_test, y_pred_test))Gwmini
import pandas as pd
from sklearn.datasets import load_breast_cancer
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LogisticRegression
from sklearn.svm import SVC
from sklearn.neighbors import KNeighborsClassifier
from sklearn.tree import DecisionTreeClassifier
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import accuracy_score, classification_report# Load the breast cancer dataset
data = load_breast_cancer()# Convert data and target to pandas dataframe
df = pd.DataFrame(data.data, columns=data.feature_names)
df['target'] = data.targetprint(df.info())# Check distribution of target variable (malignant or benign)
df['target'].value_counts().plot(kind='bar')# Explore features with boxplots (consider features you might find interesting)
df.plot(kind='box', subplots=True, figsize=(12, 12))# Split data into training and testing sets
X_train, X_test, y_train, y_test = train_test_split(df.drop('target', axis=1), df['target'], test_size=0.2)# Define and train multiple machine learning models
models = []
models.append(('Logistic Regression', LogisticRegression()))
models.append(('SVC', SVC()))
models.append(('KNN', KNeighborsClassifier()))
models.append(('Decision Tree', DecisionTreeClassifier()))
models.append(('Random Forest', RandomForestClassifier()))# Evaluate each model and print results
for name, model in models:
model.fit(X_train, y_train)
y_pred = model.predict(X_test)
print(f"\n--- {name} Results ---")
print(f"Accuracy: {accuracy_score(y_test, y_pred)}")
print(classification_report(y_test, y_pred))# Choose the best performing model based on your evaluation criteria (e.g., accuracy)
best_model_name, best_model = models[0] # Modify this to choose the best model# Train the chosen model on the entire training data
best_model.fit(X_train, y_train)# Use the best model to predict on unseen data (X_test)
y_pred = best_model.predict(X_test)# Print final evaluation results for the best model
print(f"\n--- Final Results ({best_model_name}) ---")
print(f"Accuracy: {accuracy_score(y_test, y_pred)}")
print(classification_report(y_test, y_pred))ChatGPT vs Gemini ⋮ Which is better for coding?
The code in ChatGPT mainly focuses on feature pre-processing, exploration and model selection.
- Scale features can sometimes be important for machine learning algorithms like SVM or KNN.
- ChatGPT provides us with Data like Distribution plots, Correlation heatmaps and Pairplots, to better understand or visualize something.
- The model is selected based on the cross-validation score.
Gemini AI mainly works on many models. In this case, among those models that can give you the fastest results, Gemini presents it to you.
- It doesn’t use the scale feature, which is why you often don’t see the best performance models in terms of results.
- Gemini mainly compares among many models, choosing the one that gives the most accurate result the fastest.
- Gemini doesn’t pay much attention to any model’s features. It mainly looks at the quick results of the models and based on that it publishes the results itself.
If you are interested to know the data related results of the model and from where the data is being selected then you will get good results from ChatGPT.
On the other hand, if you want the results to come to you very quickly and it doesn’t have to be so detailed, then you can use Gemini AI.
What is Google Gemini AI and How to use it? | in Easy Words 2024
Conclusion:
In this blog of ours you can see that both ChatGPT and Gemini (ChatGPT vs Gemini) AI Chatbot provide you a specific result for specific use cases. As a coder or data science expert you basically need to know what you are looking for your results on. Based on that you can use either of the two AI Chatbots for your desired results.
But in this case it can be said that both ChatGPT and Gemini (ChatGPT vs Gemini) require a certain amount of human intervention.