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Untitled.ipynb
Python 3 (ipykernel)
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@cell1 explain this code
This cell imports the necessary libraries for your machine learning project:
- pandas - For data manipulation and analysis
- numpy - For numerical operations
- matplotlib.pyplot - For data visualization
- sklearn - For machine learning algorithms
@cell2 what does this code do?
This cell loads the Iris dataset, which is a classic dataset for classification. It contains measurements of iris flowers and their species.
The code then creates a pandas DataFrame for better visualization and displays the first few rows of the data.
[1]:
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from sklearn.model_selection import train_test_split
from sklearn.ensemble import RandomForestClassifier
import numpy as np
import matplotlib.pyplot as plt
from sklearn.model_selection import train_test_split
from sklearn.ensemble import RandomForestClassifier
[2]:
# Load the Iris dataset
from sklearn.datasets import load_iris
iris = load_iris()
X = iris.data
y = iris.target
# Create a DataFrame for better visualization
df = pd.DataFrame(X, columns=iris.feature_names)
df['species'] = pd.Categorical.from_codes(y, iris.target_names)
# Display the first few rows
df.head()
from sklearn.datasets import load_iris
iris = load_iris()
X = iris.data
y = iris.target
# Create a DataFrame for better visualization
df = pd.DataFrame(X, columns=iris.feature_names)
df['species'] = pd.Categorical.from_codes(y, iris.target_names)
# Display the first few rows
df.head()
| sepal length (cm) | sepal width (cm) | petal length (cm) | petal width (cm) | species | |
|---|---|---|---|---|---|
| 0 | 5.1 | 3.5 | 1.4 | 0.2 | setosa |
| 1 | 4.9 | 3.0 | 1.4 | 0.2 | setosa |
| 2 | 4.7 | 3.2 | 1.3 | 0.2 | setosa |
| 3 | 4.6 | 3.1 | 1.5 | 0.2 | setosa |
| 4 | 5.0 | 3.6 | 1.4 | 0.2 | setosa |
[3]:
# Split the data into training and testing sets
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)
# Train a Random Forest classifier
model = RandomForestClassifier(n_estimators=100, random_state=42)
model.fit(X_train, y_train)
# Make predictions
y_pred = model.predict(X_test)
# Calculate accuracy
from sklearn.metrics import accuracy_score
accuracy = accuracy_score(y_test, y_pred)
print({"Model accuracy: {accuracy:.2f}"})
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)
# Train a Random Forest classifier
model = RandomForestClassifier(n_estimators=100, random_state=42)
model.fit(X_train, y_train)
# Make predictions
y_pred = model.predict(X_test)
# Calculate accuracy
from sklearn.metrics import accuracy_score
accuracy = accuracy_score(y_test, y_pred)
print({"Model accuracy: {accuracy:.2f}"})