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Leer Challenge | Model Building
Principal Component Analysis

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Challenge

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The task is to process the dataset and create a principal component analysis model with 3 components.

  1. Load the train.csv (from web) dataset.
  2. Drop the 'Id' column.
  3. Drop columns that contain NaN values.
  4. Standardize the dataset.
  5. Create a PCA model with 3 components for the dataset.

Oplossing

# Importing libraries
import pandas as pd
from sklearn.preprocessing import StandardScaler
from sklearn.decomposition import PCA

# Read the data
df = pd.read_csv('https://codefinity-content-media.s3.eu-west-1.amazonaws.com/7b22c447-77ad-48ae-a2d2-4e6714f7a4a6/train.csv')

# Drop the 'Id column'
df = df.drop('Id', axis = 1)

# Drop columns containing NaN values
df = df.dropna(axis = 1)

# Standardize data
df_scaled = StandardScaler().fit_transform(df)

# Create PCA model
pca_model = PCA(n_components = 3)
# Fit and transform data
df_reduced = pca_model.fit_transform(df_scaled)
print(df_reduced)

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Sectie 3. Hoofdstuk 4
# Importing libraries
import pandas as pd
from sklearn.preprocessing import StandardScaler
from sklearn.decomposition import PCA

# Read the data
df = pd.___('https://codefinity-content-media.s3.eu-west-1.amazonaws.com/7b22c447-77ad-48ae-a2d2-4e6714f7a4a6/train.csv')

# Drop the 'Id column'
df = df.___(___)

# Drop columns containing NaN values
df = df.___(___)

# Standardize data
df_scaled = ___().___(df)

# Create PCA model
pca_model = ___(n_components = ___)
# Fit and transform data
df_reduced = ___.____(___)
print(df_reduced)

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