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Structured Data Classification - Hands On Solution

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Structured Data Classification - Hands On Solution

Run the Cell to import the packages 


import pandas as pd
import numpy as np
#import dataframe as df

Data Loading Fill in the Command to load your CSV dataset "weather.csv" with pandas 


weather = pd.read_csv('weather.csv', sep=',')

Data Analysis

  • Get the shape of the dataset and print it.

  • Get the column names in list and print it.

  • Describe the dataset to understand the basic statistics of the dataset.

  • Print the first three rows of the dataset


data_size=weather.shape

print(data_size)

weather_col_names = list(weather.columns)

print(weather_col_names)

print(weather.describe())

print(weather.head(3))

Target Identification

Execute the below cell to identify the target variables. If yes it will Rain Tommorow otherwise it will not Rain.


weather_target=weather['RainTomorrow'] 

print(weather_target)

Feature Identification

In our case by analyzing the dataset, we can understand that the columns like Date might be irrelevant as they are not dependent on call usage pattern.

Since RainTomorrow is our target variable, we will be removing it from the feature set.

  • Perform appropriate operation to drop the columns Date and RainTomorrow

cols_to_drop = ['Date','RainTomorrow']

weather_feature = weather.drop(cols_to_drop,axis = 1)

print(weather_feature.head(5))

Categorical Data

In order to Identify the categorical variable in a data, use the following command in the below cell,


weather_categorical = weather.select_dtypes(include=[object])

print(weather_categorical.head(15))

Convert to boolean

Assign the column RainToday for the variable yes_no_cols and run the below cell to print first 5 rows of weather_feature


yes_no_cols = ["RainToday"]

weather_feature[yes_no_cols] = weather_feature[yes_no_cols] == 'Yes'

print(weather_feature.head(5))

One Hot Encoding

Execute the below cells to perform One Hot Encoding


weather_dumm=pd.get_dummies(weather_feature, columns=["Location","WindGustDir","WindDir9am","WindDir3pm"], prefix=["Location","WindGustDir","WindDir9am","WindDir3pm"])

weather_matrix = weather_dumm.values.astype(np.float)

Imputing-Missing Values

Do the Imputing-Missing Values by using the following parameters

  • missing_values=np.nan
  • strategy=mean
  • fill_value=None
  • verbose=0
  • copy=True

from sklearn.impute import SimpleImputer

imp=SimpleImputer(missing_values=np.nan,strategy='mean', fill_value=None,verbose=0,copy=True)

weather_matrix=imp.fit_transform(weather_matrix)

Standardization

Run the below cell to perform standardization


from sklearn.preprocessing import StandardScaler

#Standardize the data by removing the mean and scaling to unit variance

scaler = StandardScaler()

#Fit to data, then transform it.

weather_matrix = scaler.fit_transform(weather_matrix)

Train and Test Data

Splitting the data for training and testing(90% train,10% test)

  • Perform train-test split on weather_matrix and weather_target with 90% as train data and 10% as test data and set random_state as seed.

from sklearn.model_selection import train_test_split

seed=5000

train_data,test_data, train_label, test_label = train_test_split(weather_matrix,weather_target,test_size=0.1,random_state = seed)

Decision Tree Classification

  • Initialize SVM classifier with following parameters

    • kernel = linear
    • C= 0.025
    • random_state=seed

  • Train the model with train_data and train_label

  • Now predict the output with test_data

  • Evaluate the classifier with score from test_data and test_label

  • Print the predicted score


from sklearn.svm import SVC

classifier = SVC(kernel="linear",C=0.025,random_state=seed )

classifier = classifier.fit(train_data,train_label)

churn_predicted_target=classifier.predict(test_data)

score = classifier.score(test_data,test_label)

print('SVM Classifier : ',score)

with open('output.txt', 'w') as file:

    file.write(str(np.mean(score)))

Random Forest Classifier

  • Do the Random Forest Classifier of the Dataset using the following parameters.

    • max_depth=5
    • n_estimators=10
    • max_features=10
    • random_state=seed

  • Train the model with train_data and train_label.

  • Now predict the output with test_data.

  • Evaluate the classifier with score from test_data and test_label.


from sklearn.ensemble import RandomForestClassifier

classifier = RandomForestClassifier(max_depth=5,n_estimators=10,max_features=10,random_state=seed)

classifier = classifier.fit(train_data,train_label)

churn_predicted_target=classifier.predict(test_data)

score = classifier.score(test_data,test_label)

print('Random Forest Classifier : ',score)

with open('output1.txt', 'w') as file:

    file.write(str(np.mean(score)))