CODEWITHSUNDEEP
pythonmachine-learningclassificationdecision-treesupervised-learning
ebuzz168
ebuzz168

Reputation: 1194

Making a Prediction from Model Genereted by Decision Tree Algorithm

I've been trying to make a prediction that consists of a DataFrame from the model I've made using the Decision Tree algorithm.

I have got the score for my model, which is 0.96. Then, I have tried to use the model to make a prediction from DataFrame people who stay but got an error. The goal is to predict people who will leave the company in the future based on DataFrame who stay.

How to achieve that goal?

So what I did is:

  1. Read the DF from my github and splitting them to people who left and not left
df = pd.read_csv('https://raw.githubusercontent.com/bhaskoro-muthohar/DataScienceLearning/master/HR_comma_sep.csv')

leftdf = df[df['left']==1]
notleftdf =df[df['left']==0]
  1. Preparing the data for Model Generation
df.salary = df.salary.map({'low':0,'medium':1,'high':2})
df.salary

X = df.drop(['left','sales'],axis=1)
y = df['left']
  1. splitting the train and test sets
import numpy as np
from sklearn.model_selection import train_test_split


#splitting the train and test sets
X_train, X_test, y_train, y_test= train_test_split(X,y,random_state=0, stratify=y)
  1. Train it
from sklearn import tree
clftree = tree.DecisionTreeClassifier(max_depth=3)
clftree.fit(X_train,y_train)
  1. Evaluating the model
y_pred = clftree.predict(X_test)
print("Test set prediction:\n {}".format(y_pred))
print("Test set score: {:.2f}".format(clftree.score(X_test, y_test)))

The result is

Test set score: 0.96

  1. Then I'm trying to Making a prediction using the DataFrame from people who not yet left the company
X_new = notleftdf.drop(['left','sales'],axis=1)

#Map salary to 0,1,2
X_new.salary = X_new.salary.map({'low':0,'medium':1,'high':2})
X_new.salary

prediction_will_left = clftree.predict(X_new)
print("Prediction: {}".format(prediction_will_left))
print("Predicted target name: {}".format(
    notleftdf['left'][prediction_will_left]
))

The error I got is:

KeyError: "None of [Int64Index([0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n            ...\n            0, 0, 0, 0, 0, 0, 1, 0, 0, 0],\n           dtype='int64', length=11428)] are in the [index]"

How to solve it?

PS: For full script link is here

Upvotes: 3

Views: 585

Answers (1)

AKX
AKX

Reputation: 169174

Maybe you're looking for something like this. (Self-contained script once you download the data file to the same directory.)

from sklearn import tree
from sklearn.model_selection import train_test_split
import numpy as np
import pandas as pd


def process_df_for_ml(df):
    """
    Process a dataframe for model training/prediction use.

    Returns X/y tensors.
    """

    df = df.copy()
    # Map salary to 0,1,2
    df.salary = df.salary.map({"low": 0, "medium": 1, "high": 2})
    # dropping left and sales X for the df, y for the left
    X = df.drop(["left", "sales"], axis=1)
    y = df["left"]
    return (X, y)

# Read and reindex CSV.
df = pd.read_csv("HR_comma_sep.csv")
df = df.reindex()

# Train a decision tree.
X, y = process_df_for_ml(df)
X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0, stratify=y)
clftree = tree.DecisionTreeClassifier(max_depth=3)
clftree.fit(X_train, y_train)

# Test the decision tree on people who haven't left yet.
notleftdf = df[df["left"] == 0].copy()
X, y = process_df_for_ml(notleftdf)
# Plug in a new column with ones and zeroes from the prediction.
notleftdf["will_leave"] = clftree.predict(X)
# Print those with the will-leave flag on.
print(notleftdf[notleftdf["will_leave"] == 1])

Upvotes: 2

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