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Andreydemianchuk #21

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34bd291
first commit
DROBUCshK Jan 29, 2021
6428a88
made some changes
DROBUCshK Jan 29, 2021
1a370f0
added house prices task
DROBUCshK Feb 2, 2021
3c7a0f8
house prices after pep8
DROBUCshK Feb 2, 2021
68e812a
added task 3 and task 4
DROBUCshK Feb 4, 2021
f738e04
after pep8
DROBUCshK Feb 4, 2021
c7bfc0e
added task 5
DROBUCshK Feb 9, 2021
a193037
after pep8
DROBUCshK Feb 9, 2021
befeff7
after pep8 finally
DROBUCshK Feb 9, 2021
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232 changes: 232 additions & 0 deletions Andreydemianchuk_code/tasks/task_1/Titanic_s.ipynb
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{
"nbformat": 4,
"nbformat_minor": 0,
"metadata": {
"colab": {
"name": "Titanic_simple.ipynb",
"provenance": [],
"collapsed_sections": []
},
"kernelspec": {
"name": "python3",
"display_name": "Python 3"
}
},
"cells": [
{
"cell_type": "markdown",
"metadata": {
"id": "XtClpWT-RGU4"
},
"source": [
"**Importing all libraries**"
]
},
{
"cell_type": "code",
"metadata": {
"id": "QLMcjiNeO-uz"
},
"source": [
"import pandas as pd\n",
"from sklearn.ensemble import RandomForestClassifier\n",
"from sklearn.linear_model import LogisticRegression\n",
"from lightgbm import LGBMClassifier\n",
"from sklearn.preprocessing import StandardScaler\n",
"from sklearn.ensemble import VotingClassifier"
],
"execution_count": 23,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"id": "jL3ggg9IRFnT"
},
"source": [
"**Loading the data**"
]
},
{
"cell_type": "code",
"metadata": {
"id": "FhTUIFLbQ-SS"
},
"source": [
"train_data = pd.read_csv(\"/content/sample_data/train.csv\", index_col=\"PassengerId\")\n",
"test_data = pd.read_csv(\"/content/sample_data/test.csv\", index_col=\"PassengerId\")"
],
"execution_count": 24,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"id": "fW5Kzh3JRVbb"
},
"source": [
"**Feature selection**"
]
},
{
"cell_type": "code",
"metadata": {
"id": "tt5LFPViRaiR"
},
"source": [
"features = [\"Pclass\", \"Sex\", \"SibSp\", \"Parch\"]\n",
"X = pd.get_dummies(train_data[features])\n",
"X_test = pd.get_dummies(test_data[features])\n",
"y = train_data[\"Survived\"]"
],
"execution_count": 25,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"id": "6Bb17U40SG2y"
},
"source": [
"**Data Normalization**"
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "K1ZvNjfRR_IB",
"outputId": "e7397408-010b-4822-8df8-e96f7a142a9b"
},
"source": [
"ss = StandardScaler()\n",
"X_scaled = ss.fit_transform(X)\n",
"X_test_scaled = ss.transform(X_test)\n",
"print(X_scaled)\n",
"print(X_test_scaled)"
],
"execution_count": 26,
"outputs": [
{
"output_type": "stream",
"text": [
"[[ 0.82737724 0.43279337 -0.47367361 -0.73769513 0.73769513]\n",
" [-1.56610693 0.43279337 -0.47367361 1.35557354 -1.35557354]\n",
" [ 0.82737724 -0.4745452 -0.47367361 1.35557354 -1.35557354]\n",
" ...\n",
" [ 0.82737724 0.43279337 2.00893337 1.35557354 -1.35557354]\n",
" [-1.56610693 -0.4745452 -0.47367361 -0.73769513 0.73769513]\n",
" [ 0.82737724 -0.4745452 -0.47367361 -0.73769513 0.73769513]]\n",
"[[ 0.82737724 -0.4745452 -0.47367361 -0.73769513 0.73769513]\n",
" [ 0.82737724 0.43279337 -0.47367361 1.35557354 -1.35557354]\n",
" [-0.36936484 -0.4745452 -0.47367361 -0.73769513 0.73769513]\n",
" ...\n",
" [ 0.82737724 -0.4745452 -0.47367361 -0.73769513 0.73769513]\n",
" [ 0.82737724 -0.4745452 -0.47367361 -0.73769513 0.73769513]\n",
" [ 0.82737724 0.43279337 0.76762988 -0.73769513 0.73769513]]\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "j4Sdo0r_SipM"
},
"source": [
"**Modeling**"
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "LG70wCttSfdL",
"outputId": "e65c3bd8-7452-451e-ae77-5c8b9bf5095c"
},
"source": [
"rfc = RandomForestClassifier(n_estimators=100, max_depth=5, random_state=1)\n",
"rfc.fit(X_scaled, y)\n",
"lg = LogisticRegression(random_state=10, max_iter=1000, C=20, solver='lbfgs')\n",
"lg.fit(X_scaled, y)\n",
"lgb = LGBMClassifier()\n",
"lgb.fit(X_scaled, y)"
],
"execution_count": 27,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"LGBMClassifier(boosting_type='gbdt', class_weight=None, colsample_bytree=1.0,\n",
" importance_type='split', learning_rate=0.1, max_depth=-1,\n",
" min_child_samples=20, min_child_weight=0.001, min_split_gain=0.0,\n",
" n_estimators=100, n_jobs=-1, num_leaves=31, objective=None,\n",
" random_state=None, reg_alpha=0.0, reg_lambda=0.0, silent=True,\n",
" subsample=1.0, subsample_for_bin=200000, subsample_freq=0)"
]
},
"metadata": {
"tags": []
},
"execution_count": 27
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "ZcKY8aNXTpjv"
},
"source": [
"**Ensembling**"
]
},
{
"cell_type": "code",
"metadata": {
"id": "N_w1IJ-STus2"
},
"source": [
"ensemble_model = VotingClassifier(estimators=[\n",
" (\"logit\", lg),\n",
" (\"rf\", rfc),\n",
" (\"lgb\", lgb),\n",
"], voting=\"hard\")\n",
"\n",
"ensemble_model.fit(X_scaled, y)\n",
"preds = ensemble_model.predict(X_test_scaled)"
],
"execution_count": 28,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"id": "Ti0tSHUDUAUa"
},
"source": [
"**Output**"
]
},
{
"cell_type": "code",
"metadata": {
"id": "bjeO0_OGUCPb"
},
"source": [
"output = pd.DataFrame({'PassengerId': test_data.index,\n",
" 'Survived': preds})\n",
"\n",
"output.to_csv('submission.csv', index=False)"
],
"execution_count": 29,
"outputs": []
}
]
}
64 changes: 64 additions & 0 deletions Andreydemianchuk_code/tasks/task_1/titanic_s.py
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# -*- coding: utf-8 -*-
"""Titanic_simple.ipynb

Automatically generated by Colaboratory.

Original file is located at
https://colab.research.google.com/drive/1xML6OS-AeM6VQ1qkooNVCbykEKUXWAqV

**Importing all libraries**
"""

import pandas as pd
from sklearn.ensemble import RandomForestClassifier
from sklearn.linear_model import LogisticRegression
from lightgbm import LGBMClassifier
from sklearn.preprocessing import StandardScaler
from sklearn.ensemble import VotingClassifier

"""**Loading the data**"""

train_data = pd.read_csv("/content/sample_data/train.csv", index_col="PassengerId")
test_data = pd.read_csv("/content/sample_data/test.csv", index_col="PassengerId")

"""**Feature selection**"""

features = ["Pclass", "Sex", "SibSp", "Parch"]
X = pd.get_dummies(train_data[features])
X_test = pd.get_dummies(test_data[features])
y = train_data["Survived"]

"""**Data Normalization**"""

ss = StandardScaler()
X_scaled = ss.fit_transform(X)
X_test_scaled = ss.transform(X_test)
print(X_scaled)
print(X_test_scaled)

"""**Modeling**"""

rfc = RandomForestClassifier(n_estimators=100, max_depth=5, random_state=1)
rfc.fit(X_scaled, y)
lg = LogisticRegression(random_state=10, max_iter=1000, C=20, solver='lbfgs')
lg.fit(X_scaled, y)
lgb = LGBMClassifier()
lgb.fit(X_scaled, y)

"""**Ensembling**"""

ensemble_model = VotingClassifier(estimators=[
("logit", lg),
("rf", rfc),
("lgb", lgb),
], voting="hard")

ensemble_model.fit(X_scaled, y)
preds = ensemble_model.predict(X_test_scaled)

"""**Output**"""

output = pd.DataFrame({'PassengerId': test_data.index,
'Survived': preds})

output.to_csv('submission.csv', index=False)
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