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Stepak Capstone Project/Capstone_Final_Stepak.py

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+# Name: Sarah Stepak
+# Date: 04.08.2019
+# Program: capstone.py
+# Purpose: Completes the capstone project!
+
+import pandas as pd
+import numpy as np
+from matplotlib import pyplot as plt
+from sklearn import preprocessing
+from sklearn.model_selection import train_test_split
+from sklearn.metrics import accuracy_score, recall_score, precision_score, f1_score
+
+# Imports classification methods
+from sklearn.neighbors import KNeighborsClassifier
+from sklearn.svm import SVC
+# Imports regression methods
+from sklearn.linear_model import LinearRegression
+from sklearn.linear_model import Ridge
+
+# Imports the data
+df = pd.read_csv("profiles.csv")
+
+
+
+# ----    ----    ----    ----    ----    ----    ----    ----    ----    ----
+
+# Question 1: Can you predict somebody's age,
+#             based on their body type, diet, and overall health?
+
+# ----    ----    ----    ----    ----    ----    ----    ----    ----    ----
+
+# Assigning numeric values to health
+# physical health factors
+body_type_mapping = {"overweight":0.1666,
+                     "a little extra":0.3333,
+                     "thin":0.5,
+                     "average":0.6666,
+                     "athletic":0.8333,
+                     "fit":1}
+
+diet_mapping = { "strictly anything":0.1111,
+                 "mostly anything":0.2222,
+                 "anything":0.3333,
+                 "mostly vegan":0.4444,
+                 "vegan":0.5555,
+                 "strictly vegan":0.6666,
+                 "mostly vegetarian":0.7777,
+                 "vegetarian":0.8888,
+                 "strictly vegetarian":1}
+
+drink_mapping = {"desperately":0.1666,
+                 "very often":0.3333,
+                 "often":0.5,
+                 "socially":0.6666,
+                 "rarely":0.8333,
+                 "not at all":1}
+
+height_mapping = {"1.0":1,"3.0":1,"4.0":1,
+                  "6.0":0.8945,"8.0":0.8945,"9.0":0.8945,
+                  "26.0":0.7892,
+                  "36.0":0.6839,"37.0":0.6839,
+                  "42.0":0.6313,"43.0":0.6313,
+                  "47.0":0.5787,"48.0":0.5787,"49.0":0.5787,
+                  "50.0":0.5261,"51.0":0.5261,"52.0":0.5261,"53.0":0.5261,"54.0":0.5261,
+                  "55.0":0.4735,"56.0":0.4735,"57.0":0.4735,"58.0":0.4735,"59.0":0.4735,
+                  "60.0":0.4209,"61.0":0.4209,"62.0":0.4209,"63.0":0.4209,"64.0":0.4209,
+                  "65.0":0.3683,"66.0":0.3683,"67.0":0.3683,"68.0":0.3683,"69.0":0.3683,
+                  "70.0":0.3157,"71.0":0.3157,"72.0":0.3157,"73.0":0.3157,"74.0":0.3157,
+                  "75.0":0.2631,"76.0":0.2631,"77.0":0.2631,"78.0":0.2631,"79.0":0.2631,
+                  "80.0":0.2105,"81.0":0.2105,"82.0":0.2105,"83.0":0.2105,"84.0":0.2105,
+                  "85.0":0.1579,"86.0":0.1579,"87.0":0.1579,"88.0":0.1579,"89.0":0.1579,
+                  "90.0":0.1053,"91.0":0.1053,"92.0":0.1053,"93.0":0.1053,"94.0":0.1053,
+                  "95.0":0.0526}
+
+smoke_mapping = {"yes":0.1666,
+                 "when drinking":0.3333,
+                 "sometimes":0.5,
+                 "trying to quit":0.6666,
+                 "no":0.8333,
+                 "never":1}
+
+drugs_mapping = {"often":0.3333,
+                 "sometimes":0.6666,
+                 "never":1}
+
+
+#mental health factors
+child_mapping = {"doesn’t want kids":0.1666,
+                 "doesn’t have kids":0.3333,
+                 "doesn’t have kids, but might want them":0.5,
+                 "doesn’t have kids, but wants them":0.6666,
+                 "has a kid":0.8333,
+                 "has kids":1}
+
+education_mapping = {"graduated from high school":0.125,
+                     "working on space camp":0.25,
+                     "graduated from space camp":0.375,
+                     "working on two-year college":0.5,
+                     "working on college/university":0.75,
+                     "graduated from college/university":0.75,
+                     "working on masters program":0.875,
+                     "graduated from masters program":8}
+
+pets_mapping = {"likes cats":0.1429, "likes dogs":0.1429,
+                "likes dogs and dislikes cats":0.2858,
+                "dislikes dogs and likes cats":0.2858,
+                "likes dogs and likes cats":0.4287,
+                "has cats":0.5713, "has dogs":0.5713,
+                "has dogs and dislikes cats":0.7142,
+                "dislikes dogs and has cats":0.7142,
+                "has dogs and likes cats":0.8571,
+                "likes dogs and has cats":0.8571,
+                "has dogs and has cats":1}
+
+income_mapping = {"20000":0.0833,
+                  "30000":0.1666,
+                  "40000":0.2499,
+                  "60000":0.3332,
+                  "50000":0.4165,
+                  "70000":0.4998,
+                  "80000":0.5831,
+                  "100000":0.6664,
+                  "150000":0.7497,
+                  "250000":0.833,
+                  "500000":0.9163,
+                  "1000000":1}
+
+profession_mapping = {"unemployed":0.125,
+                      "rather not say":0.25,
+                      "other":0.375,
+                      "student":0.5,
+                      "transportation":0.625,
+                      "science / tech / engineering":0.75,
+                      "computer / hardware / software":0.75,
+                      "artistic / musical / writer":0.75,
+                      "sales / marketing / biz dev":0.75,
+                      "medicine / health":0.75,
+                      "education / academia":0.75,
+                      "executive / management":0.75,
+                      "banking / financial / real estate":0.75,
+                      "entertainment / media":0.75,
+                      "law / legal services":0.75,
+                      "hospitality / travel":0.75,
+                      "construction / craftsmanship":0.75,
+                      "clerical / administrative":0.75,
+                      "political / government":0.75,
+                      "military":0.875,
+                      "retired":1}
+
+
+# Data mapping
+df["body_type_data"] = df.body_type.map(body_type_mapping)
+df["child_data"] = df.offspring.map(child_mapping)
+df["diet_data"] = df.diet.map(diet_mapping)
+df["drinks_data"] = df.drinks.map(drink_mapping)
+df["drugs_data"] = df.drugs.map(drugs_mapping)
+df["education_data"] = df.education.map(education_mapping)
+df["height_data"] = df.height.map(height_mapping)
+df["income_data"] = df.income.map(income_mapping)
+df["job_data"] = df.job.map(profession_mapping)
+df["pet_data"] = df.pets.map(pets_mapping)
+df["smokes_data"] = df.smokes.map(smoke_mapping)
+
+
+# Splitting the data
+graph_scale = df[["body_type_data","diet_data","drinks_data","drugs_data","height_data","smokes_data","pet_data","job_data","income_data","education_data","child_data"]]
+graph_scale = graph_scale.replace(np.nan, 0,regex=True)
+x=graph_scale.values
+min_max_scaler = preprocessing.MinMaxScaler()
+x_scaled = min_max_scaler.fit_transform(x)
+graph_scale = pd.DataFrame(x_scaled, columns=graph_scale.columns)
+
+x = graph_scale
+y = df[["age"]]
+x_train, x_test, y_train, y_test = train_test_split(x, y,
+                                                    train_size = 0.8,
+                                                    test_size = 0.2,
+                                                    random_state=100)
+
+
+# Ridge Regression methods
+rr = Ridge(alpha=0.0001,normalize=True)
+rr.fit(x_train, y_train)
+y_predict = rr.predict(x_test)
+plt.scatter(y_test, y_predict, alpha=0.4)
+
+# Plots the data
+plt.xlabel("Age")
+plt.ylabel("Predicted Age")
+plt.title("Actual vs Predicted Age, Based on Overall Health Factors")
+plt.show()
+
+# Accuracy and Precision
+print(plt.score(x_train,y_train))
+print(plt.score(x_test,y_test))
+
+#Multi-Linear Regression
+#mlr = LinearRegression()
+#mlr.fit(x_train, y_train)
+#y_predict = mlr.predict(x_test)
+
+#Modeling the Data
+#model=mlr.fit(x_train, y_train)
+#y_predict = mlr.predict(x_test)
+#plt.scatter(y_test, y_predict, alpha=0.4)
+
+
+# ----    ----    ----    ----    ----    ----    ----    ----    ----    ----
+
+# Question 2: Can you predict somebody's sex,
+#             based on their sign and their income level?
+
+# ----    ----    ----    ----    ----    ----    ----    ----    ----    ----
+
+sex_mapping = {"m":0, "f":1}
+
+sign_mapping = {"capricorn and it’s fun to think about":1,
+                "capricorn but it doesn’t matter":1,
+                "capricorn":1,
+                "capricorn and it matters a lot":1,
+                "aquarius and it’s fun to think about":2,
+                "aquarius but it doesn’t matter":2,
+                "aquarius":2,
+                "aquarius and it matters a lot":2,
+                "pisces and it’s fun to think about":3,
+                "pisces but it doesn’t matter":3,
+                "pisces":3,
+                "pisces and it matters a lot":3,
+                "aries and it’s fun to think about":4,
+                "aries but it doesn’t matter":4,
+                "aries":4,
+                "aries and it matters a lot":4,
+                "taurus and it’s fun to think about":5,
+                "taurus but it doesn’t matter":5,
+                "taurus":5,
+                "taurus and it matters a lot":5,
+                "gemini and it’s fun to think about":6,
+                "gemini but it doesn’t matter":6,
+                "gemini":6,
+                "gemini and it matters a lot":6,
+                "cancer and it’s fun to think about":7,
+                "cancer but it doesn’t matter":7,
+                "cancer":7,
+                "cancer and it matters a lot":7,
+                "leo and it’s fun to think about":8,
+                "leo but it doesn’t matter":8,
+                "leo":8,
+                "leo and it matters a lot":8,
+                "virgo and it’s fun to think about":9,
+                "virgo but it doesn’t matter":9,
+                "virgo":9,
+                "virgo and it matters a lot":9,
+                "libra and it’s fun to think about":10,
+                "libra but it doesn’t matter":10,
+                "libra":10,
+                "libra and it matters a lot":10,
+                "scorpio and it’s fun to think about":11,
+                "scorpio but it doesn’t matter":11,
+                "scorpio":11,
+                "scorpio and it matters a lot":11,
+                "sagittarius and it’s fun to think about":12,
+                "sagittarius but it doesn’t matter":12,
+                "sagittarius":12,
+                "sagittarius and it matters a lot":12}
+
+#Maps the data]
+df["gender_data"] = df.sex.map(sex_mapping)
+df["sign_data"] = df.sign.map(sign_mapping)
+
+# Splitting the data
+graph_scale = df[["body_type_data","sign_data"]]
+graph_scale = graph_scale.replace(np.nan, 0,regex=True)
+x=graph_scale.values
+min_max_scaler = preprocessing.MinMaxScaler()
+x_scaled = min_max_scaler.fit_transform(x)
+graph_scale = pd.DataFrame(x_scaled, columns=graph_scale.columns)
+x = graph_scale
+y = df[["gender_data"]]
+x_train, x_test, y_train, y_test = train_test_split(x, y,
+                                                    train_size = 0.8,
+                                                    test_size = 0.2,
+                                                    random_state=100)
+# Support Vector
+#classifier = SVC(kernel = 'linear')
+#classifier.fit(x_train,y_train)
+#y_predict = classifier.predict(x_test)
+
+# K-Neighbors
+classifier = KNeighborsClassifier(n_neighbors = 80)
+classifier.fit(x_train,y_train)
+y_predict = classifier.predict(x_test)
+
+# Plots the data
+plt.xlabel("Gender")
+plt.ylabel("Predicted Gender")
+plt.title("Actual vs Predicted Gender")
+plt.plot(y_test,y_predict)
+plt.show()
+
+# Accuracy, Precision, Recall, F1
+print(accuracy_score(y_test,y_predict))
+print(recall_score(y_test,y_predict))
+print(precision_score(y_test,y_predict))
+print(f1_score(y_test,y_predict))