ML Example Using SKLearn

input.csv

ML_Example.py

import numpy as np
import pandas as pd
import sklearn
from sklearn import tree

df = pd.read_csv("/data/input.csv")

texture_map = {"bumpy":0, "smooth":1}
df["texture"] = df["texture"].map(lambda x: texture_map[x])

label_map = {"orange":0, "apple":1}
df["label"] = df["label"].map(lambda x: label_map[x])

print(df.head())

clf = tree.DecisionTreeClassifier()

input_X = df[["weight", "texture"]]

input_y = df["label"].values

clf.fit(input_X, input_y)

print(clf.predict([[160,0]]))