alathrop

# DataCamp ML for Marketing in Python

'''
Fit decision tree model

Now you will fit a decision tree on the training set of the telecom dataset, and then predict labels on the unseen testing data, and calculate the accuracy of your model predictions. You will see the difference in the performance compared to the logistic regression.

The accuracy_score function has been imported, also the training and testing datasets that you've built previously have been loaded as train_X and test_X for features, and train_Y and test_Y for target variables.
'''
# Initialize decision tree classifier

alathrop

# DataCamp ML for Marketing in Python

# Print the unique Churn values
print(set(telcom['Churn']))

# Calculate the ratio size of each churn group
telcom.groupby(['Churn']).size() / telcom.shape[0] * 100

# Import the function for splitting data to train and test
from sklearn.model_selection import train_test_split

alathrop

# DataCamp sklearn supervised

# Import scale
from sklearn.preprocessing import scale

# Scale the features: X_scaled
X_scaled = scale(X)

# Print the mean and standard deviation of the unscaled features
print("Mean of Unscaled Features: {}".format(np.mean(X))) 

alathrop

# DataCamp sklearn supervised

# Import pandas
import pandas as pd

# Read 'gapminder.csv' into a DataFrame: df
df = pd.read_csv('gapminder.csv')

# Create a boxplot of life expectancy per region
df.boxplot('life', 'Region', rot=60)

alathrop

## Data Camp supervised learning with sklearn ##
# Import necessary modules
from sklearn.linear_model import LogisticRegression
from sklearn.model_selection import GridSearchCV

# Setup the hyperparameter grid
c_space = np.logspace(-5, 8, 15)
param_grid = {'C': c_space}

# Instantiate a logistic regression classifier: logreg

alathrop

## Data Camp supervised learning with sklearn ##
# Import the necessary modules
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import confusion_matrix, classification_report

# Create training and test sets
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.4, random_state=42)

# Create the classifier: logreg
logreg = LogisticRegression()

alathrop

## Data Camp supervised learning with sklearn ##

# Import necessary modules
from sklearn.metrics import classification_report, confusion_matrix

# Create training and test set
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.4, random_state=42)

# Instantiate a k-NN classifier: knn
knn = KNeighborsClassifier(n_neighbors=6)

alathrop

\!h ''' Manning Get Programming with Python | unit 6, module 4 | analyze your friends'''

def read_file(file):
    """
    file: a file object
    Starting from the first line, it reads every 2 lines and stores
    them in a tuple. Starting from the second line, it reads every
    2 lines and stores them in a tuple.
    Returns a tuple of the two tuples.
    """

alathrop

\!h ''' Manning Get Programming with Python | unit 6, module 3 | advanced operations with functions'''

def normalize_to_100(score, out_of):
    """
    score: integer representing a score
    out_of: integer representing what score is out of
    Returns normalized score so that it is out of 100
    """
    return score*100/out_of
    

alathrop

''' Manning Get Programming with Python | unit 6, module 3 advanced operations with functions'''

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