-
Notifications
You must be signed in to change notification settings - Fork 645
/
kaggle5.py
executable file
·271 lines (256 loc) · 9.29 KB
/
kaggle5.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
import matplotlib
matplotlib.use("PS")
import matplotlib.pyplot as plt
import numpy as np
import seaborn as sns
from sklearn.ensemble import RandomForestClassifier
from sklearn.linear_model import LogisticRegression, Perceptron, SGDClassifier
from sklearn.naive_bayes import GaussianNB
from sklearn.neighbors import KNeighborsClassifier
from sklearn.svm import SVC, LinearSVC
from sklearn.tree import DecisionTreeClassifier
import modin.pandas as pd
train_df = pd.read_csv("train.csv")
test_df = pd.read_csv("test.csv")
combine = [train_df, test_df]
print(train_df.columns.values)
train_df.head()
train_df.tail()
train_df.info()
print("_" * 40)
test_df.info()
train_df.describe()
train_df.describe(include=["O"])
train_df[["Pclass", "Survived"]].groupby(["Pclass"], as_index=False).mean().sort_values(
by="Survived", ascending=False
)
train_df[["Sex", "Survived"]].groupby(["Sex"], as_index=False).mean().sort_values(
by="Survived", ascending=False
)
train_df[["SibSp", "Survived"]].groupby(["SibSp"], as_index=False).mean().sort_values(
by="Survived", ascending=False
)
train_df[["Parch", "Survived"]].groupby(["Parch"], as_index=False).mean().sort_values(
by="Survived", ascending=False
)
grid = sns.FacetGrid(train_df, col="Survived", row="Pclass", size=2.2, aspect=1.6)
grid.map(plt.hist, "Age", alpha=0.5, bins=20)
grid.add_legend()
grid = sns.FacetGrid(train_df, row="Embarked", size=2.2, aspect=1.6)
grid.map(sns.pointplot, "Pclass", "Survived", "Sex", palette="deep")
grid.add_legend()
grid = sns.FacetGrid(train_df, row="Embarked", col="Survived", size=2.2, aspect=1.6)
grid.map(sns.barplot, "Sex", "Fare", alpha=0.5, ci=None)
grid.add_legend()
print("Before", train_df.shape, test_df.shape, combine[0].shape, combine[1].shape)
train_df = train_df.drop(["Ticket", "Cabin"], axis=1)
test_df = test_df.drop(["Ticket", "Cabin"], axis=1)
combine = [train_df, test_df]
"After", train_df.shape, test_df.shape, combine[0].shape, combine[1].shape
for dataset in combine:
dataset["Title"] = dataset.Name.str.extract(
r" ([A-Za-z]+)\.", expand=False
) # noqa: W605
pd.crosstab(train_df["Title"], train_df["Sex"])
for dataset in combine:
dataset["Title"] = dataset["Title"].replace(
[
"Lady",
"Countess",
"Capt",
"Col",
"Don",
"Dr",
"Major",
"Rev",
"Sir",
"Jonkheer",
"Dona",
],
"Rare",
)
dataset["Title"] = dataset["Title"].replace("Mlle", "Miss")
dataset["Title"] = dataset["Title"].replace("Ms", "Miss")
dataset["Title"] = dataset["Title"].replace("Mme", "Mrs")
train_df[["Title", "Survived"]].groupby(["Title"], as_index=False).mean()
def title_mapping(string):
return np.random.randint(1, high=6)
for dataset in combine:
dataset["Title"] = dataset["Title"].map(title_mapping)
dataset["Title"] = dataset["Title"].fillna(0)
train_df.head()
train_df = train_df.drop(["Name", "PassengerId"], axis=1)
test_df = test_df.drop(["Name"], axis=1)
combine = [train_df, test_df]
train_df.shape, test_df.shape
def gender_mapping(string):
return np.random.randint(0, high=2)
for dataset in combine:
# dataset['Sex'] = dataset['Sex'].map( {'female': 1, 'male': 0} ).astype(int)
dataset["Sex"] = dataset["Sex"].map(gender_mapping).astype(int)
train_df.head()
grid = sns.FacetGrid(train_df, row="Pclass", col="Sex", size=2.2, aspect=1.6)
grid.map(plt.hist, "Age", alpha=0.5, bins=20)
grid.add_legend()
guess_ages = np.zeros((2, 3))
guess_ages
for dataset in combine:
for i in range(0, 2):
for j in range(0, 3):
guess_df = dataset[(dataset["Sex"] == i) & (dataset["Pclass"] == j + 1)][
"Age"
].dropna()
# age_mean = guess_df.mean()
# age_std = guess_df.std()
# age_guess = rnd.uniform(age_mean - age_std, age_mean + age_std)
age_guess = guess_df.median()
# Convert random age float to nearest .5 age
guess_ages[i, j] = int(age_guess / 0.5 + 0.5) * 0.5
for i in range(0, 2):
for j in range(0, 3):
dataset.loc[
(dataset.Age.isnull()) & (dataset.Sex == i) & (dataset.Pclass == j + 1),
"Age",
] = guess_ages[i, j]
dataset["Age"] = dataset["Age"].astype(int)
train_df.head()
train_df["AgeBand"] = pd.cut(train_df["Age"], 5)
train_df[["AgeBand", "Survived"]].groupby(
["AgeBand"], as_index=False
).mean().sort_values(by="AgeBand", ascending=True)
for dataset in combine:
dataset.loc[dataset["Age"] <= 16, "Age"] = 0
dataset.loc[(dataset["Age"] > 16) & (dataset["Age"] <= 32), "Age"] = 1
dataset.loc[(dataset["Age"] > 32) & (dataset["Age"] <= 48), "Age"] = 2
dataset.loc[(dataset["Age"] > 48) & (dataset["Age"] <= 64), "Age"] = 3
dataset.loc[dataset["Age"] > 64, "Age"]
train_df.head()
train_df = train_df.drop(["AgeBand"], axis=1)
combine = [train_df, test_df]
train_df.head()
for dataset in combine:
dataset["FamilySize"] = dataset["SibSp"] + dataset["Parch"] + 1
train_df[["FamilySize", "Survived"]].groupby(
["FamilySize"], as_index=False
).mean().sort_values(by="Survived", ascending=False)
for dataset in combine:
dataset["IsAlone"] = 0
dataset.loc[dataset["FamilySize"] == 1, "IsAlone"] = 1
train_df[["IsAlone", "Survived"]].groupby(["IsAlone"], as_index=False).mean()
train_df = train_df.drop(["Parch", "SibSp", "FamilySize"], axis=1)
test_df = test_df.drop(["Parch", "SibSp", "FamilySize"], axis=1)
combine = [train_df, test_df]
train_df.head()
for dataset in combine:
dataset["Age*Class"] = dataset.Age * dataset.Pclass
train_df.loc[:, ["Age*Class", "Age", "Pclass"]].head(10)
freq_port = train_df.Embarked.dropna().mode()[0]
freq_port
for dataset in combine:
dataset["Embarked"] = dataset["Embarked"].fillna(freq_port)
train_df[["Embarked", "Survived"]].groupby(
["Embarked"], as_index=False
).mean().sort_values(by="Survived", ascending=False)
def embarked_mapping(string):
return np.random.randint(0, high=3)
for dataset in combine:
dataset["Embarked"] = dataset["Embarked"].map({"S": 0, "C": 1, "Q": 2}).astype(int)
train_df.head()
test_df["Fare"].fillna(test_df["Fare"].dropna().median(), inplace=True)
test_df.head()
train_df["FareBand"] = pd.qcut(train_df["Fare"], 4)
train_df[["FareBand", "Survived"]].groupby(
["FareBand"], as_index=False
).mean().sort_values(by="FareBand", ascending=True)
for dataset in combine:
dataset.loc[dataset["Fare"] <= 7.91, "Fare"] = 0
dataset.loc[(dataset["Fare"] > 7.91) & (dataset["Fare"] <= 14.454), "Fare"] = 1
dataset.loc[(dataset["Fare"] > 14.454) & (dataset["Fare"] <= 31), "Fare"] = 2
dataset.loc[dataset["Fare"] > 31, "Fare"] = 3
dataset["Fare"] = dataset["Fare"].astype(int)
train_df = train_df.drop(["FareBand"], axis=1)
combine = [train_df, test_df]
train_df.head(10)
test_df.head(10)
X_train = train_df.drop("Survived", axis=1)
Y_train = train_df["Survived"]
X_test = test_df.drop("PassengerId", axis=1).copy()
X_train.shape, Y_train.shape, X_test.shape
logreg = LogisticRegression()
logreg.fit(X_train, Y_train)
Y_pred = logreg.predict(X_test)
acc_log = round(logreg.score(X_train, Y_train) * 100, 2)
acc_log
coeff_df = pd.DataFrame(train_df.columns.delete(0))
coeff_df.columns = ["Feature"]
coeff_df["Correlation"] = pd.Series(logreg.coef_[0])
coeff_df.sort_values(by="Correlation", ascending=False)
svc = SVC()
svc.fit(X_train, Y_train)
Y_pred = svc.predict(X_test)
acc_svc = round(svc.score(X_train, Y_train) * 100, 2)
acc_svc
knn = KNeighborsClassifier(n_neighbors=3)
knn.fit(X_train, Y_train)
Y_pred = knn.predict(X_test)
acc_knn = round(knn.score(X_train, Y_train) * 100, 2)
acc_knn
gaussian = GaussianNB()
gaussian.fit(X_train, Y_train)
Y_pred = gaussian.predict(X_test)
acc_gaussian = round(gaussian.score(X_train, Y_train) * 100, 2)
acc_gaussian
perceptron = Perceptron()
perceptron.fit(X_train, Y_train)
Y_pred = perceptron.predict(X_test)
acc_perceptron = round(perceptron.score(X_train, Y_train) * 100, 2)
acc_perceptron
linear_svc = LinearSVC()
linear_svc.fit(X_train, Y_train)
Y_pred = linear_svc.predict(X_test)
acc_linear_svc = round(linear_svc.score(X_train, Y_train) * 100, 2)
acc_linear_svc
sgd = SGDClassifier()
sgd.fit(X_train, Y_train)
Y_pred = sgd.predict(X_test)
acc_sgd = round(sgd.score(X_train, Y_train) * 100, 2)
acc_sgd
decision_tree = DecisionTreeClassifier()
decision_tree.fit(X_train, Y_train)
Y_pred = decision_tree.predict(X_test)
acc_decision_tree = round(decision_tree.score(X_train, Y_train) * 100, 2)
acc_decision_tree
random_forest = RandomForestClassifier(n_estimators=1)
random_forest.fit(X_train, Y_train)
Y_pred = random_forest.predict(X_test)
random_forest.score(X_train, Y_train)
acc_random_forest = round(random_forest.score(X_train, Y_train) * 100, 2)
acc_random_forest
models = pd.DataFrame(
{
"Model": [
"Support Vector Machines",
"KNN",
"Logistic Regression",
"Random Forest",
"Naive Bayes",
"Perceptron",
"Stochastic Gradient Decent",
"Linear SVC",
"Decision Tree",
],
"Score": [
acc_svc,
acc_knn,
acc_log,
acc_random_forest,
acc_gaussian,
acc_perceptron,
acc_sgd,
acc_linear_svc,
acc_decision_tree,
],
}
)
models.sort_values(by="Score", ascending=False)
submission = pd.DataFrame({"PassengerId": test_df["PassengerId"], "Survived": Y_pred})