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Predicting_Food_Delivery_Time.ipynb
{
"nbformat": 4,
"nbformat_minor": 0,
"metadata": {
"colab": {
"name": "Article_Predicting_Food_Del_Time.ipynb",
"provenance": [],
"collapsed_sections": [
"dBV2JLd1ALQH",
"UceqrG8LAFr_",
"_YYJ_HI9A1qg",
"pwjvNSkCBLC6",
"Hjn_pdlpBD8w",
"u1kumctM5Jf5",
"SCbszxL0-NPg",
"wRVErMt0HBFp",
"tQXtDXaONwjx",
"1R2AgdQg5OKR"
]
},
"kernelspec": {
"name": "python3",
"display_name": "Python 3"
},
"accelerator": "GPU"
},
"cells": [
{
"cell_type": "markdown",
"metadata": {
"id": "YFpFT7cpAS4i",
"colab_type": "text"
},
"source": [
"# Solving MachineHack's 'Predicting Food Delivery Time - Hackathon by IMS Proschool'\n",
"\n",
"---\n",
"The entire world is transforming digitally and our relationship with technology has grown exponentially over the last few years. We have grown closer to technology, and it has made our life a lot easier by saving time and effort. Today everything is accessible with smartphones — from groceries to cooked food and from medicines to doctors. In this hackathon, we provide you with data that is a by-product as well as a thriving proof of this growing relationship. \n",
"\n",
"When was the last time you ordered food online? And how long did it take to reach you?\n",
"\n",
"In this hackathon, we are providing you with data from thousands of restaurants in India regarding the time they take to deliver food for online order. As data scientists, your goal is to predict the online order delivery time based on the given factors.\n",
"\n",
"Analytics India Magazine and IMS Proschool bring to you [‘Predicting Predicting Food Delivery Time Hackathon’](https://www.machinehack.com/course/predicting-food-delivery-time-hackathon-by-ims-proschool/).\n",
"\n",
"Size of training set: 11,094 records\n",
"\n",
"Size of test set: 2,774 records\n",
"\n",
"**FEATURES:**\n",
"\n",
"* Restaurant: A unique ID that represents a restaurant.\n",
"* Location: The location of the restaurant.\n",
"* Cuisines: The cuisines offered by the restaurant.\n",
"* Average_Cost: The average cost for one person/order.\n",
"* Minimum_Order: The minimum order amount.\n",
"* Rating: Customer rating for the restaurant.\n",
"* Votes: The total number of customer votes for the restaurant.\n",
"* Reviews: The number of customer reviews for the restaurant.\n",
"* Delivery_Time: The order delivery time of the restaurant. (Target Classes) \n",
"\n",
"About IMS Proschool\n",
"\n",
"IMS, since 1977, has worked towards building a long term successful career for its students. It emerged as the fourth most trusted education brands in an AC Nielsen and Brand Equity Survey. IMS Proschool is the extension of the same mission. Proschool helps individuals realize their potential by mentoring and imparting skills.\n",
"\n",
"**Click [here](https://www.machinehack.com/course/predicting-food-delivery-time-hackathon-by-ims-proschool/) to participate.**\n",
" "
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "S_jBYc1XPmJ3",
"colab_type": "text"
},
"source": [
"##Mounting Google Drive "
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "pe6bXU0rQUqC",
"colab_type": "text"
},
"source": [
"Mount your google drive to access files in the drive, here I have uploaded all the requires datasets in to my google drive, mounted it to colab by authorising access."
]
},
{
"cell_type": "code",
"metadata": {
"id": "dfsrnSdXiyjg",
"colab_type": "code",
"outputId": "3e457e1a-a3d0-4730-9f27-c9d2b5338e82",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 122
}
},
"source": [
"from google.colab import drive\n",
"drive.mount(\"/GD\")"
],
"execution_count": 1,
"outputs": [
{
"output_type": "stream",
"text": [
"Go to this URL in a browser: https://accounts.google.com/o/oauth2/auth?client_id=947318989803-6bn6qk8qdgf4n4g3pfee6491hc0brc4i.apps.googleusercontent.com&redirect_uri=urn%3aietf%3awg%3aoauth%3a2.0%3aoob&response_type=code&scope=email%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdocs.test%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdrive%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdrive.photos.readonly%20https%3a%2f%2fwww.googleapis.com%2fauth%2fpeopleapi.readonly\n",
"\n",
"Enter your authorization code:\n",
"··········\n",
"Mounted at /GD\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "dBV2JLd1ALQH",
"colab_type": "text"
},
"source": [
"##Loading The Data"
]
},
{
"cell_type": "code",
"metadata": {
"id": "lDkaeJm_j1R0",
"colab_type": "code",
"colab": {}
},
"source": [
"#Importing necessary libraries\n",
"import pandas as pd\n",
"import numpy as np\n",
"import re\n",
"from tqdm import tqdm"
],
"execution_count": 0,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"id": "iPqcGP9PQqnC",
"colab_type": "text"
},
"source": [
"We will now load the datasets using pandas by mentioning the path to the files residing in the drive as given below."
]
},
{
"cell_type": "code",
"metadata": {
"id": "dqndqEBzjReJ",
"colab_type": "code",
"colab": {}
},
"source": [
"train = pd.read_excel(\"/GD/My Drive/Colab Notebooks/Food_delivery_Time_Prediction/DataSets/Data_Train.xlsx\")\n",
"test = pd.read_excel(\"/GD/My Drive/Colab Notebooks/Food_delivery_Time_Prediction/DataSets/Data_Test.xlsx\")"
],
"execution_count": 0,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"id": "UceqrG8LAFr_",
"colab_type": "text"
},
"source": [
"##Dataset Features"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "5L36MM2DRJNz",
"colab_type": "text"
},
"source": [
"Before proceeding any further,lets have a look at the dataset and its basic features.\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "kRIwn_yRAJVE",
"colab_type": "text"
},
"source": [
"####Trainig Data"
]
},
{
"cell_type": "code",
"metadata": {
"id": "PkH-VCvOj0Ai",
"colab_type": "code",
"outputId": "2bdf3686-9e88-4649-c6e2-397ea567c2c6",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 0
}
},
"source": [
"train.shape"
],
"execution_count": 4,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"(11094, 9)"
]
},
"metadata": {
"tags": []
},
"execution_count": 4
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "Fi1vTZOjRT_2",
"colab_type": "code",
"outputId": "01407b70-d0b0-4e52-afbd-35441dd2b7c3",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 0
}
},
"source": [
"train.columns"
],
"execution_count": 5,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"Index(['Restaurant', 'Location', 'Cuisines', 'Average_Cost', 'Minimum_Order',\n",
" 'Rating', 'Votes', 'Reviews', 'Delivery_Time'],\n",
" dtype='object')"
]
},
"metadata": {
"tags": []
},
"execution_count": 5
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "S-u0Y6aHlRmn",
"colab_type": "code",
"outputId": "40505abe-d266-48b6-a56e-369ab329d8ba",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 0
}
},
"source": [
"train.head()"
],
"execution_count": 6,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Restaurant</th>\n",
" <th>Location</th>\n",
" <th>Cuisines</th>\n",
" <th>Average_Cost</th>\n",
" <th>Minimum_Order</th>\n",
" <th>Rating</th>\n",
" <th>Votes</th>\n",
" <th>Reviews</th>\n",
" <th>Delivery_Time</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>ID_6321</td>\n",
" <td>FTI College, Law College Road, Pune</td>\n",
" <td>Fast Food, Rolls, Burger, Salad, Wraps</td>\n",
" <td>₹200</td>\n",
" <td>₹50</td>\n",
" <td>3.5</td>\n",
" <td>12</td>\n",
" <td>4</td>\n",
" <td>30 minutes</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>ID_2882</td>\n",
" <td>Sector 3, Marathalli</td>\n",
" <td>Ice Cream, Desserts</td>\n",
" <td>₹100</td>\n",
" <td>₹50</td>\n",
" <td>3.5</td>\n",
" <td>11</td>\n",
" <td>4</td>\n",
" <td>30 minutes</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>ID_1595</td>\n",
" <td>Mumbai Central</td>\n",
" <td>Italian, Street Food, Fast Food</td>\n",
" <td>₹150</td>\n",
" <td>₹50</td>\n",
" <td>3.6</td>\n",
" <td>99</td>\n",
" <td>30</td>\n",
" <td>65 minutes</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>ID_5929</td>\n",
" <td>Sector 1, Noida</td>\n",
" <td>Mughlai, North Indian, Chinese</td>\n",
" <td>₹250</td>\n",
" <td>₹99</td>\n",
" <td>3.7</td>\n",
" <td>176</td>\n",
" <td>95</td>\n",
" <td>30 minutes</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>ID_6123</td>\n",
" <td>Rmz Centennial, I Gate, Whitefield</td>\n",
" <td>Cafe, Beverages</td>\n",
" <td>₹200</td>\n",
" <td>₹99</td>\n",
" <td>3.2</td>\n",
" <td>521</td>\n",
" <td>235</td>\n",
" <td>65 minutes</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Restaurant Location ... Reviews Delivery_Time\n",
"0 ID_6321 FTI College, Law College Road, Pune ... 4 30 minutes\n",
"1 ID_2882 Sector 3, Marathalli ... 4 30 minutes\n",
"2 ID_1595 Mumbai Central ... 30 65 minutes\n",
"3 ID_5929 Sector 1, Noida ... 95 30 minutes\n",
"4 ID_6123 Rmz Centennial, I Gate, Whitefield ... 235 65 minutes\n",
"\n",
"[5 rows x 9 columns]"
]
},
"metadata": {
"tags": []
},
"execution_count": 6
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "zg-AtOlZ_nRZ",
"colab_type": "code",
"outputId": "c35f42a3-ab13-4005-e3d0-363b373e4958",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 0
}
},
"source": [
"train.isnull().sum()"
],
"execution_count": 7,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"Restaurant 0\n",
"Location 0\n",
"Cuisines 0\n",
"Average_Cost 0\n",
"Minimum_Order 0\n",
"Rating 0\n",
"Votes 0\n",
"Reviews 0\n",
"Delivery_Time 0\n",
"dtype: int64"
]
},
"metadata": {
"tags": []
},
"execution_count": 7
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "MDc0ws0igEqr",
"colab_type": "code",
"outputId": "abdb10c4-99a9-4e96-e529-f75619010cc2",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 0
}
},
"source": [
"train.describe(include = 'all')"
],
"execution_count": 8,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Restaurant</th>\n",
" <th>Location</th>\n",
" <th>Cuisines</th>\n",
" <th>Average_Cost</th>\n",
" <th>Minimum_Order</th>\n",
" <th>Rating</th>\n",
" <th>Votes</th>\n",
" <th>Reviews</th>\n",
" <th>Delivery_Time</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>count</th>\n",
" <td>11094</td>\n",
" <td>11094</td>\n",
" <td>11094</td>\n",
" <td>11094</td>\n",
" <td>11094</td>\n",
" <td>11094</td>\n",
" <td>11094</td>\n",
" <td>11094</td>\n",
" <td>11094</td>\n",
" </tr>\n",
" <tr>\n",
" <th>unique</th>\n",
" <td>7480</td>\n",
" <td>35</td>\n",
" <td>2179</td>\n",
" <td>26</td>\n",
" <td>18</td>\n",
" <td>33</td>\n",
" <td>1103</td>\n",
" <td>761</td>\n",
" <td>7</td>\n",
" </tr>\n",
" <tr>\n",
" <th>top</th>\n",
" <td>ID_7184</td>\n",
" <td>Mico Layout, Stage 2, BTM Layout,Bangalore</td>\n",
" <td>North Indian</td>\n",
" <td>₹200</td>\n",
" <td>₹50</td>\n",
" <td>-</td>\n",
" <td>-</td>\n",
" <td>-</td>\n",
" <td>30 minutes</td>\n",
" </tr>\n",
" <tr>\n",
" <th>freq</th>\n",
" <td>22</td>\n",
" <td>947</td>\n",
" <td>850</td>\n",
" <td>3241</td>\n",
" <td>10118</td>\n",
" <td>1191</td>\n",
" <td>2074</td>\n",
" <td>2312</td>\n",
" <td>7406</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Restaurant ... Delivery_Time\n",
"count 11094 ... 11094\n",
"unique 7480 ... 7\n",
"top ID_7184 ... 30 minutes\n",
"freq 22 ... 7406\n",
"\n",
"[4 rows x 9 columns]"
]
},
"metadata": {
"tags": []
},
"execution_count": 8
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "nnlbbxSa_75y",
"colab_type": "code",
"outputId": "b5e674b5-d624-43c9-aaa0-a8054daf270a",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 0
}
},
"source": [
"train.info()"
],
"execution_count": 9,
"outputs": [
{
"output_type": "stream",
"text": [
"<class 'pandas.core.frame.DataFrame'>\n",
"RangeIndex: 11094 entries, 0 to 11093\n",
"Data columns (total 9 columns):\n",
"Restaurant 11094 non-null object\n",
"Location 11094 non-null object\n",
"Cuisines 11094 non-null object\n",
"Average_Cost 11094 non-null object\n",
"Minimum_Order 11094 non-null object\n",
"Rating 11094 non-null object\n",
"Votes 11094 non-null object\n",
"Reviews 11094 non-null object\n",
"Delivery_Time 11094 non-null object\n",
"dtypes: object(9)\n",
"memory usage: 780.2+ KB\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "ucWulRUiAaO-",
"colab_type": "text"
},
"source": [
"####Test Data"
]
},
{
"cell_type": "code",
"metadata": {
"id": "R2hWiEHllP7D",
"colab_type": "code",
"outputId": "8a75e388-5168-47c2-8135-04008aac1c51",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 0
}
},
"source": [
"test.shape"
],
"execution_count": 10,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"(2774, 8)"
]
},
"metadata": {
"tags": []
},
"execution_count": 10
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "ECOLxRPMRaZa",
"colab_type": "code",
"outputId": "7565f6f6-d749-484f-b61c-c54c46339814",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 0
}
},
"source": [
"test.columns"
],
"execution_count": 11,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"Index(['Restaurant', 'Location', 'Cuisines', 'Average_Cost', 'Minimum_Order',\n",
" 'Rating', 'Votes', 'Reviews'],\n",
" dtype='object')"
]
},
"metadata": {
"tags": []
},
"execution_count": 11
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "kA709EfflT1M",
"colab_type": "code",
"outputId": "8e848e5c-57cb-4785-83cf-89d6dd9bc667",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 0
}
},
"source": [
"test.head()"
],
"execution_count": 12,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Restaurant</th>\n",
" <th>Location</th>\n",
" <th>Cuisines</th>\n",
" <th>Average_Cost</th>\n",
" <th>Minimum_Order</th>\n",
" <th>Rating</th>\n",
" <th>Votes</th>\n",
" <th>Reviews</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>ID_2842</td>\n",
" <td>Mico Layout, Stage 2, BTM Layout,Bangalore</td>\n",
" <td>North Indian, Chinese, Assamese</td>\n",
" <td>₹350</td>\n",
" <td>₹50</td>\n",
" <td>4.2</td>\n",
" <td>361</td>\n",
" <td>225</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>ID_730</td>\n",
" <td>Mico Layout, Stage 2, BTM Layout,Bangalore</td>\n",
" <td>Biryani, Kebab</td>\n",
" <td>₹100</td>\n",
" <td>₹50</td>\n",
" <td>NEW</td>\n",
" <td>-</td>\n",
" <td>-</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>ID_4620</td>\n",
" <td>Sector 1, Noida</td>\n",
" <td>Fast Food</td>\n",
" <td>₹100</td>\n",
" <td>₹50</td>\n",
" <td>3.6</td>\n",
" <td>36</td>\n",
" <td>16</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>ID_5470</td>\n",
" <td>Babarpur, New Delhi, Delhi</td>\n",
" <td>Mithai, North Indian, Chinese, Fast Food, Sout...</td>\n",
" <td>₹200</td>\n",
" <td>₹50</td>\n",
" <td>3.6</td>\n",
" <td>66</td>\n",
" <td>33</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>ID_3249</td>\n",
" <td>Sector 1, Noida</td>\n",
" <td>Chinese, Fast Food</td>\n",
" <td>₹150</td>\n",
" <td>₹50</td>\n",
" <td>2.9</td>\n",
" <td>38</td>\n",
" <td>14</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Restaurant Location ... Votes Reviews\n",
"0 ID_2842 Mico Layout, Stage 2, BTM Layout,Bangalore ... 361 225\n",
"1 ID_730 Mico Layout, Stage 2, BTM Layout,Bangalore ... - -\n",
"2 ID_4620 Sector 1, Noida ... 36 16\n",
"3 ID_5470 Babarpur, New Delhi, Delhi ... 66 33\n",
"4 ID_3249 Sector 1, Noida ... 38 14\n",
"\n",
"[5 rows x 8 columns]"
]
},
"metadata": {
"tags": []
},
"execution_count": 12
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "cV8c6gTx_y1_",
"colab_type": "code",
"outputId": "8eba573e-4857-4ba9-948a-f8b577ad95f8",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 0
}
},
"source": [
"test.isnull().sum()"
],
"execution_count": 13,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"Restaurant 0\n",
"Location 0\n",
"Cuisines 0\n",
"Average_Cost 0\n",
"Minimum_Order 0\n",
"Rating 0\n",
"Votes 0\n",
"Reviews 0\n",
"dtype: int64"
]
},
"metadata": {
"tags": []
},
"execution_count": 13
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "vMQHK61o___h",
"colab_type": "code",
"outputId": "b56bf996-6227-4840-fcec-83a7b7299628",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 0
}
},
"source": [
"test.info()"
],
"execution_count": 14,
"outputs": [
{
"output_type": "stream",
"text": [
"<class 'pandas.core.frame.DataFrame'>\n",
"RangeIndex: 2774 entries, 0 to 2773\n",
"Data columns (total 8 columns):\n",
"Restaurant 2774 non-null object\n",
"Location 2774 non-null object\n",
"Cuisines 2774 non-null object\n",
"Average_Cost 2774 non-null object\n",
"Minimum_Order 2774 non-null object\n",
"Rating 2774 non-null object\n",
"Votes 2774 non-null object\n",
"Reviews 2774 non-null object\n",
"dtypes: object(8)\n",
"memory usage: 173.5+ KB\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "mIMWYFUDAgAv",
"colab_type": "code",
"outputId": "fcf0a501-2e5b-4d6d-9eb5-0fd1b65edf70",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 0
}
},
"source": [
"test.describe(include = 'all')"
],
"execution_count": 15,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Restaurant</th>\n",
" <th>Location</th>\n",
" <th>Cuisines</th>\n",
" <th>Average_Cost</th>\n",
" <th>Minimum_Order</th>\n",
" <th>Rating</th>\n",
" <th>Votes</th>\n",
" <th>Reviews</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>count</th>\n",
" <td>2774</td>\n",
" <td>2774</td>\n",
" <td>2774</td>\n",
" <td>2774</td>\n",
" <td>2774</td>\n",
" <td>2774</td>\n",
" <td>2774</td>\n",
" <td>2774</td>\n",
" </tr>\n",
" <tr>\n",
" <th>unique</th>\n",
" <td>2401</td>\n",
" <td>35</td>\n",
" <td>881</td>\n",
" <td>19</td>\n",
" <td>9</td>\n",
" <td>30</td>\n",
" <td>580</td>\n",
" <td>392</td>\n",
" </tr>\n",
" <tr>\n",
" <th>top</th>\n",
" <td>ID_1209</td>\n",
" <td>D-Block, Sector 63, Noida</td>\n",
" <td>North Indian</td>\n",
" <td>₹200</td>\n",
" <td>₹50</td>\n",
" <td>-</td>\n",
" <td>-</td>\n",
" <td>-</td>\n",
" </tr>\n",
" <tr>\n",
" <th>freq</th>\n",
" <td>8</td>\n",
" <td>221</td>\n",
" <td>226</td>\n",
" <td>820</td>\n",
" <td>2556</td>\n",
" <td>305</td>\n",
" <td>542</td>\n",
" <td>593</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Restaurant Location Cuisines ... Rating Votes Reviews\n",
"count 2774 2774 2774 ... 2774 2774 2774\n",
"unique 2401 35 881 ... 30 580 392\n",
"top ID_1209 D-Block, Sector 63, Noida North Indian ... - - -\n",
"freq 8 221 226 ... 305 542 593\n",
"\n",
"[4 rows x 8 columns]"
]
},
"metadata": {
"tags": []
},
"execution_count": 15
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "_YYJ_HI9A1qg",
"colab_type": "text"
},
"source": [
"##Data Cleaning"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "yyJYTvr8RjwO",
"colab_type": "text"
},
"source": [
"We will consider the following features of training and test datasets. Here I have grouped the columns based on similarity of values which will make it easy for cleaning.\n",
"\n",
"* Cleaning Average_Cost & Minimum_order columns to remove special characters and make them float variables.\n",
"* Location & Cuisines are categoriacal variables that needs to be cleaned before encoding.\n",
"* Rating, Votes & Reviews needs to be cleaned and converted to respective types.\n",
"\n",
"We will delve deeper in the coming sections.\n",
"\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "pwjvNSkCBLC6",
"colab_type": "text"
},
"source": [
"####Average_Cost\t& Minimum_order"
]
},
{
"cell_type": "code",
"metadata": {
"id": "sRsfJuPbkW3C",
"colab_type": "code",
"outputId": "202817ab-bba9-4d7a-915b-cffd229f1c2e",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 0
}
},
"source": [
"train.head()"
],
"execution_count": 16,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Restaurant</th>\n",
" <th>Location</th>\n",
" <th>Cuisines</th>\n",
" <th>Average_Cost</th>\n",
" <th>Minimum_Order</th>\n",
" <th>Rating</th>\n",
" <th>Votes</th>\n",
" <th>Reviews</th>\n",
" <th>Delivery_Time</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>ID_6321</td>\n",
" <td>FTI College, Law College Road, Pune</td>\n",
" <td>Fast Food, Rolls, Burger, Salad, Wraps</td>\n",
" <td>₹200</td>\n",
" <td>₹50</td>\n",
" <td>3.5</td>\n",
" <td>12</td>\n",
" <td>4</td>\n",
" <td>30 minutes</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>ID_2882</td>\n",
" <td>Sector 3, Marathalli</td>\n",
" <td>Ice Cream, Desserts</td>\n",
" <td>₹100</td>\n",
" <td>₹50</td>\n",
" <td>3.5</td>\n",
" <td>11</td>\n",
" <td>4</td>\n",
" <td>30 minutes</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>ID_1595</td>\n",
" <td>Mumbai Central</td>\n",
" <td>Italian, Street Food, Fast Food</td>\n",
" <td>₹150</td>\n",
" <td>₹50</td>\n",
" <td>3.6</td>\n",
" <td>99</td>\n",
" <td>30</td>\n",
" <td>65 minutes</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>ID_5929</td>\n",
" <td>Sector 1, Noida</td>\n",
" <td>Mughlai, North Indian, Chinese</td>\n",
" <td>₹250</td>\n",
" <td>₹99</td>\n",
" <td>3.7</td>\n",
" <td>176</td>\n",
" <td>95</td>\n",
" <td>30 minutes</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>ID_6123</td>\n",
" <td>Rmz Centennial, I Gate, Whitefield</td>\n",
" <td>Cafe, Beverages</td>\n",
" <td>₹200</td>\n",
" <td>₹99</td>\n",
" <td>3.2</td>\n",
" <td>521</td>\n",
" <td>235</td>\n",
" <td>65 minutes</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Restaurant Location ... Reviews Delivery_Time\n",
"0 ID_6321 FTI College, Law College Road, Pune ... 4 30 minutes\n",
"1 ID_2882 Sector 3, Marathalli ... 4 30 minutes\n",
"2 ID_1595 Mumbai Central ... 30 65 minutes\n",
"3 ID_5929 Sector 1, Noida ... 95 30 minutes\n",
"4 ID_6123 Rmz Centennial, I Gate, Whitefield ... 235 65 minutes\n",
"\n",
"[5 rows x 9 columns]"
]
},
"metadata": {
"tags": []
},
"execution_count": 16
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "tqNZCFbcm15K",
"colab_type": "code",
"outputId": "ee9e23c6-9e9e-440d-87ed-4a22acf24c25",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 0
}
},
"source": [
"#Finding the unique values in Average_Cost\n",
"train['Average_Cost'].unique()"
],
"execution_count": 17,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"array(['₹200', '₹100', '₹150', '₹250', '₹650', '₹350', '₹800', '₹50',\n",
" '₹400', '₹600', '₹300', '₹750', '₹450', '₹550', '₹1,000', '₹500',\n",
" '₹900', '₹1,200', '₹950', '₹850', '₹700', '₹1,150', 'for',\n",
" '₹1,100', '₹1,400', '₹2,050'], dtype=object)"
]
},
"metadata": {
"tags": []
},
"execution_count": 17
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "XCtKvvfUTRqn",
"colab_type": "text"
},
"source": [
"The Average_Cost has an invalid value in one of its rows. We will replace it with 200 which is the most frequent value in the column.(Check the train.describe() method). We will add 200 as a string and not as an integer as the column is of type object and all its values are strings."
]
},
{
"cell_type": "code",
"metadata": {
"id": "9eFmcCodty24",
"colab_type": "code",
"outputId": "4091a454-e401-4ff9-e565-abc7b3bc0f67",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 0
}
},
"source": [
"train[train['Average_Cost'] == 'for']"
],
"execution_count": 18,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Restaurant</th>\n",
" <th>Location</th>\n",
" <th>Cuisines</th>\n",
" <th>Average_Cost</th>\n",
" <th>Minimum_Order</th>\n",
" <th>Rating</th>\n",
" <th>Votes</th>\n",
" <th>Reviews</th>\n",
" <th>Delivery_Time</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>6297</th>\n",
" <td>ID_6472</td>\n",
" <td>Pune University</td>\n",
" <td>Fast Food</td>\n",
" <td>for</td>\n",
" <td>₹50</td>\n",
" <td>NEW</td>\n",
" <td>-</td>\n",
" <td>-</td>\n",
" <td>30 minutes</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Restaurant Location Cuisines ... Votes Reviews Delivery_Time\n",
"6297 ID_6472 Pune University Fast Food ... - - 30 minutes\n",
"\n",
"[1 rows x 9 columns]"
]
},
"metadata": {
"tags": []
},
"execution_count": 18
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "YxAdTpeojHJF",
"colab_type": "code",
"colab": {}
},
"source": [
"#replacing 'for' with 200\n",
"train['Average_Cost'].replace('for', '200', inplace = True)"
],
"execution_count": 0,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"id": "Mo-S0e7HT2T2",
"colab_type": "text"
},
"source": [
"Now we will clean all the values and will convert it in to integer. "
]
},
{
"cell_type": "code",
"metadata": {
"id": "eoJghDs-OB-K",
"colab_type": "code",
"colab": {}
},
"source": [
"train['Average_Cost_Cleaned'] = train['Average_Cost'].apply(lambda x: int(re.sub(\"[^0-9]\", \"\", x)))"
],
"execution_count": 0,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "H_POcn32wdck",
"colab_type": "code",
"outputId": "d8b64898-25ff-4268-af37-425546c0de41",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 0
}
},
"source": [
"train['Average_Cost_Cleaned'].unique()"
],
"execution_count": 21,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"array([ 200, 100, 150, 250, 650, 350, 800, 50, 400, 600, 300,\n",
" 750, 450, 550, 1000, 500, 900, 1200, 950, 850, 700, 1150,\n",
" 1100, 1400, 2050])"
]
},
"metadata": {
"tags": []
},
"execution_count": 21
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "iNlGUAMuUTye",
"colab_type": "text"
},
"source": [
"We can see that all the special characters ahave been removed and strings have been converted to integers.\n",
"\n",
"---\n",
"\n",
"Let's perform the same operations on the test set\n"
]
},
{
"cell_type": "code",
"metadata": {
"id": "wCZxzEqSOB8D",
"colab_type": "code",
"outputId": "e31782eb-c8c9-4dc2-836a-d28e2839fe1b",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 0
}
},
"source": [
"test['Average_Cost'].unique()"
],
"execution_count": 22,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"array(['₹350', '₹100', '₹200', '₹150', '₹300', '₹50', '₹250', '₹500',\n",
" '₹650', '₹400', '₹550', '₹450', '₹600', '₹750', '₹850', '₹1,000',\n",
" '₹700', '₹800', '₹1,200'], dtype=object)"
]
},
"metadata": {
"tags": []
},
"execution_count": 22
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "Q4gYXWVhOB6S",
"colab_type": "code",
"colab": {}
},
"source": [
"test['Average_Cost_Cleaned'] = test['Average_Cost'].apply(lambda x: int(re.sub(\"[^0-9]\", \"\", x)))"
],
"execution_count": 0,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "hmDLCsnVwoLi",
"colab_type": "code",
"outputId": "c2bbdb37-252c-44c6-b041-f4570a920912",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 0
}
},
"source": [
"test['Average_Cost_Cleaned'].unique()"
],
"execution_count": 24,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"array([ 350, 100, 200, 150, 300, 50, 250, 500, 650, 400, 550,\n",
" 450, 600, 750, 850, 1000, 700, 800, 1200])"
]
},
"metadata": {
"tags": []
},
"execution_count": 24
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "0TtRyWCAUwfC",
"colab_type": "text"
},
"source": [
"Now lets do the same for Minimum_Order column"
]
},
{
"cell_type": "code",
"metadata": {
"id": "XgKZKZYEJHdu",
"colab_type": "code",
"outputId": "224edb78-b2f8-4e54-baea-1cb62f88c2c4",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 0
}
},
"source": [
"train['Minimum_Order'].unique()"
],
"execution_count": 25,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"array(['₹50', '₹99', '₹0', '₹200', '₹450', '₹350', '₹79', '₹400', '₹199',\n",
" '₹500', '₹250', '₹150', '₹90', '₹299', '₹300', '₹240', '₹89',\n",
" '₹59'], dtype=object)"
]
},
"metadata": {
"tags": []
},
"execution_count": 25
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "P3a-jxJDUNXu",
"colab_type": "code",
"colab": {}
},
"source": [
"train['Minimum_Order_Cleaned'] = train['Minimum_Order'].apply(lambda x: int(re.sub(\"[^0-9]\", \"\", x)))"
],
"execution_count": 0,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "2KZl-XPAUNV3",
"colab_type": "code",
"outputId": "43765787-d4ed-4c0d-a0cc-e617e5059b22",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 0
}
},
"source": [
"test['Minimum_Order'].unique()"
],
"execution_count": 27,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"array(['₹50', '₹99', '₹500', '₹0', '₹200', '₹149', '₹199', '₹399', '₹89'],\n",
" dtype=object)"
]
},
"metadata": {
"tags": []
},
"execution_count": 27
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "uZMbJwFnUNTh",
"colab_type": "code",
"colab": {}
},
"source": [
"test['Minimum_Order_Cleaned'] = test['Minimum_Order'].apply(lambda x: int(re.sub(\"[^0-9]\", \"\", x)))"
],
"execution_count": 0,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "NRddLSAzUNRu",
"colab_type": "code",
"outputId": "b1485f03-9677-44c8-9319-b7d03ecc1d84",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 0
}
},
"source": [
"train.head()"
],
"execution_count": 29,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Restaurant</th>\n",
" <th>Location</th>\n",
" <th>Cuisines</th>\n",
" <th>Average_Cost</th>\n",
" <th>Minimum_Order</th>\n",
" <th>Rating</th>\n",
" <th>Votes</th>\n",
" <th>Reviews</th>\n",
" <th>Delivery_Time</th>\n",
" <th>Average_Cost_Cleaned</th>\n",
" <th>Minimum_Order_Cleaned</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>ID_6321</td>\n",
" <td>FTI College, Law College Road, Pune</td>\n",
" <td>Fast Food, Rolls, Burger, Salad, Wraps</td>\n",
" <td>₹200</td>\n",
" <td>₹50</td>\n",
" <td>3.5</td>\n",
" <td>12</td>\n",
" <td>4</td>\n",
" <td>30 minutes</td>\n",
" <td>200</td>\n",
" <td>50</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>ID_2882</td>\n",
" <td>Sector 3, Marathalli</td>\n",
" <td>Ice Cream, Desserts</td>\n",
" <td>₹100</td>\n",
" <td>₹50</td>\n",
" <td>3.5</td>\n",
" <td>11</td>\n",
" <td>4</td>\n",
" <td>30 minutes</td>\n",
" <td>100</td>\n",
" <td>50</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>ID_1595</td>\n",
" <td>Mumbai Central</td>\n",
" <td>Italian, Street Food, Fast Food</td>\n",
" <td>₹150</td>\n",
" <td>₹50</td>\n",
" <td>3.6</td>\n",
" <td>99</td>\n",
" <td>30</td>\n",
" <td>65 minutes</td>\n",
" <td>150</td>\n",
" <td>50</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>ID_5929</td>\n",
" <td>Sector 1, Noida</td>\n",
" <td>Mughlai, North Indian, Chinese</td>\n",
" <td>₹250</td>\n",
" <td>₹99</td>\n",
" <td>3.7</td>\n",
" <td>176</td>\n",
" <td>95</td>\n",
" <td>30 minutes</td>\n",
" <td>250</td>\n",
" <td>99</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>ID_6123</td>\n",
" <td>Rmz Centennial, I Gate, Whitefield</td>\n",
" <td>Cafe, Beverages</td>\n",
" <td>₹200</td>\n",
" <td>₹99</td>\n",
" <td>3.2</td>\n",
" <td>521</td>\n",
" <td>235</td>\n",
" <td>65 minutes</td>\n",
" <td>200</td>\n",
" <td>99</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Restaurant ... Minimum_Order_Cleaned\n",
"0 ID_6321 ... 50\n",
"1 ID_2882 ... 50\n",
"2 ID_1595 ... 50\n",
"3 ID_5929 ... 99\n",
"4 ID_6123 ... 99\n",
"\n",
"[5 rows x 11 columns]"
]
},
"metadata": {
"tags": []
},
"execution_count": 29
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "Hjn_pdlpBD8w",
"colab_type": "text"
},
"source": [
"####Location & CUISINES"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "1S2LHft5U9YN",
"colab_type": "text"
},
"source": [
"Location and Cuisines are categorical variables that need to be encoded later. By looking at the dataset, we can see that each of these columns have multiple values in them. We can use each of them as a feature by splitting each column into n number of features.\n",
"To do that we will first find the maximum number(n) of features a column has in the entire dataset including both test and train data. Once the maximum number of features within a cell is found, we will split all the rows in the dataset for that specific column into n features. \n"
]
},
{
"cell_type": "code",
"metadata": {
"id": "TlVQYHp0Sldz",
"colab_type": "code",
"colab": {}
},
"source": [
"#A function to find the maximun number of features in a single cell\n",
"def max_features_in_single_row(train, test, delimiter):\n",
" max_info = 0 \n",
" item_lis = list(train.append(test))\n",
" for i in item_lis:\n",
" if len(i.split(\"{}\".format(delimiter))) > max_info:\n",
" max_info = len(i.split(\"{}\".format(delimiter)))\n",
" print(\"\\n\",\"-\"*35) \n",
" print(\"Max_Features in One Observation = \", max_info)\n",
" return max_info"
],
"execution_count": 0,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "3eNvsPsPlVjZ",
"colab_type": "code",
"colab": {}
},
"source": [
"#This function splits a column in to n features where n is the maximum number of features in a single cell\n",
"def feature_splitter(feat, name, delimiter, max_info):\n",
" item_lis = list(feat)\n",
" extracted_features = {}\n",
"\n",
" for i in range(max_info):\n",
" extracted_features['{}_Feature_{}'.format(name, i+1)] = []\n",
" \n",
" print(\"-\"*35)\n",
" print(\"Features Dictionary : \", extracted_features)\n",
"\n",
" for i in tqdm(range(len(item_lis))):\n",
" for j in range(max_info): \n",
" try:\n",
" extracted_features['{}_Feature_{}'.format(name,j+1)].append(item_lis[i].split(\"{}\".format(delimiter))[j].lower().strip())\n",
" except: \n",
" extracted_features['{}_Feature_{}'.format(name, j+1)].append(np.nan)\n",
"\n",
"\n",
" return extracted_features\n"
],
"execution_count": 0,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "hIgyWIdKNiXi",
"colab_type": "code",
"outputId": "64442708-4d44-4611-961b-dab0b2393f1b",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 0
}
},
"source": [
"#Splitting Location\n",
"loc_max = max_features_in_single_row(test['Location'],train['Location'], ',')\n",
"train_Location_splits = feature_splitter(train['Location'], 'Location', ',', loc_max)\n",
"test_Location_splits = feature_splitter(test['Location'], 'Location', ',', loc_max)\n"
],
"execution_count": 32,
"outputs": [
{
"output_type": "stream",
"text": [
"100%|██████████| 11094/11094 [00:00<00:00, 175718.95it/s]\n",
"100%|██████████| 2774/2774 [00:00<00:00, 145604.94it/s]"
],
"name": "stderr"
},
{
"output_type": "stream",
"text": [
"\n",
" -----------------------------------\n",
"Max_Features in One Observation = 4\n",
"-----------------------------------\n",
"Features Dictionary : {'Location_Feature_1': [], 'Location_Feature_2': [], 'Location_Feature_3': [], 'Location_Feature_4': []}\n",
"-----------------------------------\n",
"Features Dictionary : {'Location_Feature_1': [], 'Location_Feature_2': [], 'Location_Feature_3': [], 'Location_Feature_4': []}\n"
],
"name": "stdout"
},
{
"output_type": "stream",
"text": [
"\n"
],
"name": "stderr"
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "KqcwdG_gkCPa",
"colab_type": "code",
"outputId": "cdeb9f06-6126-4521-c894-9168faf526b4",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 0
}
},
"source": [
"#Splitting Cuisines\n",
"cus_max = max_features_in_single_row(test['Cuisines'],train['Cuisines'], ',')\n",
"train_Cuisines_splits = feature_splitter(train['Cuisines'], 'Cuisines', ',', cus_max)\n",
"test_Cuisines_splits = feature_splitter(test['Cuisines'], 'Cuisines', ',', cus_max)"
],
"execution_count": 33,
"outputs": [
{
"output_type": "stream",
"text": [
"\r 0%| | 0/11094 [00:00<?, ?it/s]"
],
"name": "stderr"
},
{
"output_type": "stream",
"text": [
"\n",
" -----------------------------------\n",
"Max_Features in One Observation = 8\n",
"-----------------------------------\n",
"Features Dictionary : {'Cuisines_Feature_1': [], 'Cuisines_Feature_2': [], 'Cuisines_Feature_3': [], 'Cuisines_Feature_4': [], 'Cuisines_Feature_5': [], 'Cuisines_Feature_6': [], 'Cuisines_Feature_7': [], 'Cuisines_Feature_8': []}\n"
],
"name": "stdout"
},
{
"output_type": "stream",
"text": [
"100%|██████████| 11094/11094 [00:00<00:00, 83184.55it/s]\n",
"100%|██████████| 2774/2774 [00:00<00:00, 81339.74it/s]"
],
"name": "stderr"
},
{
"output_type": "stream",
"text": [
"-----------------------------------\n",
"Features Dictionary : {'Cuisines_Feature_1': [], 'Cuisines_Feature_2': [], 'Cuisines_Feature_3': [], 'Cuisines_Feature_4': [], 'Cuisines_Feature_5': [], 'Cuisines_Feature_6': [], 'Cuisines_Feature_7': [], 'Cuisines_Feature_8': []}\n"
],
"name": "stdout"
},
{
"output_type": "stream",
"text": [
"\n"
],
"name": "stderr"
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "telTCWSRUvZp",
"colab_type": "text"
},
"source": [
"####Rating, Votes & Reviews"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "br9VvuoNW0nB",
"colab_type": "text"
},
"source": [
"We will now clean Rating, Votes & Reviews columns to remove invalid values and to convert them to the right type."
]
},
{
"cell_type": "code",
"metadata": {
"id": "pdSxC7iJVJVA",
"colab_type": "code",
"outputId": "f720fd4f-cc41-4978-f4b4-ef8075ea29c5",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 0
}
},
"source": [
"train.describe(include = 'all')"
],
"execution_count": 34,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Restaurant</th>\n",
" <th>Location</th>\n",
" <th>Cuisines</th>\n",
" <th>Average_Cost</th>\n",
" <th>Minimum_Order</th>\n",
" <th>Rating</th>\n",
" <th>Votes</th>\n",
" <th>Reviews</th>\n",
" <th>Delivery_Time</th>\n",
" <th>Average_Cost_Cleaned</th>\n",
" <th>Minimum_Order_Cleaned</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>count</th>\n",
" <td>11094</td>\n",
" <td>11094</td>\n",
" <td>11094</td>\n",
" <td>11094</td>\n",
" <td>11094</td>\n",
" <td>11094</td>\n",
" <td>11094</td>\n",
" <td>11094</td>\n",
" <td>11094</td>\n",
" <td>11094.000000</td>\n",
" <td>11094.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>unique</th>\n",
" <td>7480</td>\n",
" <td>35</td>\n",
" <td>2179</td>\n",
" <td>26</td>\n",
" <td>18</td>\n",
" <td>33</td>\n",
" <td>1103</td>\n",
" <td>761</td>\n",
" <td>7</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>top</th>\n",
" <td>ID_7184</td>\n",
" <td>Mico Layout, Stage 2, BTM Layout,Bangalore</td>\n",
" <td>North Indian</td>\n",
" <td>₹200</td>\n",
" <td>₹50</td>\n",
" <td>-</td>\n",
" <td>-</td>\n",
" <td>-</td>\n",
" <td>30 minutes</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>freq</th>\n",
" <td>22</td>\n",
" <td>947</td>\n",
" <td>850</td>\n",
" <td>3241</td>\n",
" <td>10118</td>\n",
" <td>1191</td>\n",
" <td>2074</td>\n",
" <td>2312</td>\n",
" <td>7406</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>mean</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>202.708671</td>\n",
" <td>53.344511</td>\n",
" </tr>\n",
" <tr>\n",
" <th>std</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>129.833261</td>\n",
" <td>18.551245</td>\n",
" </tr>\n",
" <tr>\n",
" <th>min</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>50.000000</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25%</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>100.000000</td>\n",
" <td>50.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>50%</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>200.000000</td>\n",
" <td>50.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>75%</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>200.000000</td>\n",
" <td>50.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>max</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>2050.000000</td>\n",
" <td>500.000000</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Restaurant ... Minimum_Order_Cleaned\n",
"count 11094 ... 11094.000000\n",
"unique 7480 ... NaN\n",
"top ID_7184 ... NaN\n",
"freq 22 ... NaN\n",
"mean NaN ... 53.344511\n",
"std NaN ... 18.551245\n",
"min NaN ... 0.000000\n",
"25% NaN ... 50.000000\n",
"50% NaN ... 50.000000\n",
"75% NaN ... 50.000000\n",
"max NaN ... 500.000000\n",
"\n",
"[11 rows x 11 columns]"
]
},
"metadata": {
"tags": []
},
"execution_count": 34
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "98JSZquOUuhA",
"colab_type": "code",
"colab": {}
},
"source": [
"#A function to find all the non numeric values\n",
"def non_numerals(series):\n",
" non_numerals = []\n",
" for i in series.unique():\n",
" try :\n",
" i = float(i)\n",
" except:\n",
" non_numerals.append(i)\n",
" return non_numerals"
],
"execution_count": 0,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "Xd-2qqH7dPcD",
"colab_type": "code",
"colab": {}
},
"source": [
"# A function to replace the non-numeric values\n",
"def replace_nn_with(series, type_, fill_with = None, method = 'mean'):\n",
"\n",
" nn = non_numerals(series)\n",
" print('-'*30)\n",
" print('-'*30)\n",
" print(\"Non Numerals in column \",series.name,\" : \",nn)\n",
"\n",
" series = series.replace(nn, np.nan, inplace = False)\n",
" nulls = series.isnull().sum()\n",
" if fill_with:\n",
" series.fillna(fill_with, inplace = True)\n",
" print(\"Filling Non Numerals with {}\".format(fill_with))\n",
" \n",
" else:\n",
" series = series.replace(nn, np.nan, inplace = False)\n",
"\n",
" if method == 'mean' :\n",
" rep = series.astype(float).mean()\n",
" print(\"Filling Non Numerals with MEAN = \", rep)\n",
"\n",
" elif method == 'median' :\n",
" rep = series.astype(float).median()\n",
" print(\"Filling Non Numerals with MEDIAN = \", rep)\n",
"\n",
" elif method == 'min' :\n",
" rep = series.astype(float).min()\n",
" print(\"Filling Non Numerals with MINIMUM = \", rep)\n",
"\n",
" else:\n",
" print('Please pass a valid method as a string -- (\"mean\" or \"median\" or \"min\")')\n",
" return 0\n",
"\n",
" series.fillna(rep, inplace = True)\n",
" \n",
" try:\n",
" series = series.astype(type_)\n",
" print(nulls, \": observations replaced\")\n",
" return series\n",
" except:\n",
" # Since type conversion of a string containting decimals to int is not possible, it is first converted to float\n",
" series = series.astype(float)\n",
" print(nulls, \": observations replaced\")\n",
" series = series.astype(type_)\n",
" return series\n",
" \n"
],
"execution_count": 0,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "UgP5SZBbvH-n",
"colab_type": "code",
"outputId": "53209dd0-ef36-4746-fc39-c1179049135b",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 0
}
},
"source": [
"train.head()"
],
"execution_count": 37,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Restaurant</th>\n",
" <th>Location</th>\n",
" <th>Cuisines</th>\n",
" <th>Average_Cost</th>\n",
" <th>Minimum_Order</th>\n",
" <th>Rating</th>\n",
" <th>Votes</th>\n",
" <th>Reviews</th>\n",
" <th>Delivery_Time</th>\n",
" <th>Average_Cost_Cleaned</th>\n",
" <th>Minimum_Order_Cleaned</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>ID_6321</td>\n",
" <td>FTI College, Law College Road, Pune</td>\n",
" <td>Fast Food, Rolls, Burger, Salad, Wraps</td>\n",
" <td>₹200</td>\n",
" <td>₹50</td>\n",
" <td>3.5</td>\n",
" <td>12</td>\n",
" <td>4</td>\n",
" <td>30 minutes</td>\n",
" <td>200</td>\n",
" <td>50</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>ID_2882</td>\n",
" <td>Sector 3, Marathalli</td>\n",
" <td>Ice Cream, Desserts</td>\n",
" <td>₹100</td>\n",
" <td>₹50</td>\n",
" <td>3.5</td>\n",
" <td>11</td>\n",
" <td>4</td>\n",
" <td>30 minutes</td>\n",
" <td>100</td>\n",
" <td>50</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>ID_1595</td>\n",
" <td>Mumbai Central</td>\n",
" <td>Italian, Street Food, Fast Food</td>\n",
" <td>₹150</td>\n",
" <td>₹50</td>\n",
" <td>3.6</td>\n",
" <td>99</td>\n",
" <td>30</td>\n",
" <td>65 minutes</td>\n",
" <td>150</td>\n",
" <td>50</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>ID_5929</td>\n",
" <td>Sector 1, Noida</td>\n",
" <td>Mughlai, North Indian, Chinese</td>\n",
" <td>₹250</td>\n",
" <td>₹99</td>\n",
" <td>3.7</td>\n",
" <td>176</td>\n",
" <td>95</td>\n",
" <td>30 minutes</td>\n",
" <td>250</td>\n",
" <td>99</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>ID_6123</td>\n",
" <td>Rmz Centennial, I Gate, Whitefield</td>\n",
" <td>Cafe, Beverages</td>\n",
" <td>₹200</td>\n",
" <td>₹99</td>\n",
" <td>3.2</td>\n",
" <td>521</td>\n",
" <td>235</td>\n",
" <td>65 minutes</td>\n",
" <td>200</td>\n",
" <td>99</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Restaurant ... Minimum_Order_Cleaned\n",
"0 ID_6321 ... 50\n",
"1 ID_2882 ... 50\n",
"2 ID_1595 ... 50\n",
"3 ID_5929 ... 99\n",
"4 ID_6123 ... 99\n",
"\n",
"[5 rows x 11 columns]"
]
},
"metadata": {
"tags": []
},
"execution_count": 37
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "D2UGyd1YXR_w",
"colab_type": "text"
},
"source": [
"Lets Clean the columns"
]
},
{
"cell_type": "code",
"metadata": {
"id": "N0hBJrrPcnAN",
"colab_type": "code",
"outputId": "630054fe-d55c-4134-e0e4-177a4c857c52",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 0
}
},
"source": [
"train['Rating_Cleaned'] = replace_nn_with(train['Rating'],float, method = 'mean')"
],
"execution_count": 38,
"outputs": [
{
"output_type": "stream",
"text": [
"------------------------------\n",
"------------------------------\n",
"Non Numerals in column Rating : ['-', 'NEW', 'Opening Soon', 'Temporarily Closed']\n",
"Filling Non Numerals with MEAN = 3.6134596429744668\n",
"1963 : observations replaced\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "mDNBE-N3yZFq",
"colab_type": "code",
"outputId": "8b59d664-8b52-46c5-97a8-efa854124c6b",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 0
}
},
"source": [
"test['Rating_Cleaned'] = replace_nn_with(test['Rating'],float, fill_with = 3.6134596429744668)"
],
"execution_count": 39,
"outputs": [
{
"output_type": "stream",
"text": [
"------------------------------\n",
"------------------------------\n",
"Non Numerals in column Rating : ['NEW', '-', 'Opening Soon']\n",
"Filling Non Numerals with 3.6134596429744668\n",
"507 : observations replaced\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "SrSqjkJ9cm46",
"colab_type": "code",
"outputId": "88051f3f-fb9a-4ed2-a1e7-90f47b971e44",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 0
}
},
"source": [
"train['Votes_Cleaned'] = replace_nn_with(train['Votes'],int,method = 'mean')\n"
],
"execution_count": 40,
"outputs": [
{
"output_type": "stream",
"text": [
"------------------------------\n",
"------------------------------\n",
"Non Numerals in column Votes : ['-']\n",
"Filling Non Numerals with MEAN = 244.54445676274943\n",
"2074 : observations replaced\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "NK45gKPj4m9L",
"colab_type": "code",
"outputId": "da8d1a78-1908-4ead-c0c1-32e27b115977",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 0
}
},
"source": [
"test['Votes_Cleaned'] = replace_nn_with(test['Votes'],int,fill_with = 244.54445676274943)"
],
"execution_count": 41,
"outputs": [
{
"output_type": "stream",
"text": [
"------------------------------\n",
"------------------------------\n",
"Non Numerals in column Votes : ['-']\n",
"Filling Non Numerals with 244.54445676274943\n",
"542 : observations replaced\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "D3ZTqnwIzmHa",
"colab_type": "code",
"outputId": "c667137b-e331-47fe-829c-28fd8303fdcf",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 0
}
},
"source": [
"train['Reviews_Cleaned'] = replace_nn_with(train['Reviews'],int, method = 'mean')"
],
"execution_count": 42,
"outputs": [
{
"output_type": "stream",
"text": [
"------------------------------\n",
"------------------------------\n",
"Non Numerals in column Reviews : ['-']\n",
"Filling Non Numerals with MEAN = 123.24789341835573\n",
"2312 : observations replaced\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "iEMVlV2R4vcW",
"colab_type": "code",
"outputId": "b67fb709-9059-45f4-88cf-98958019d389",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 0
}
},
"source": [
"test['Reviews_Cleaned'] = replace_nn_with(test['Reviews'],int, method = 'mean',fill_with = 123.247893 )"
],
"execution_count": 43,
"outputs": [
{
"output_type": "stream",
"text": [
"------------------------------\n",
"------------------------------\n",
"Non Numerals in column Reviews : ['-']\n",
"Filling Non Numerals with 123.247893\n",
"593 : observations replaced\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "kXVltCBZwXly",
"colab_type": "code",
"outputId": "9d8aa181-981b-4817-c4ca-1ca57a6eee49",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 0
}
},
"source": [
"train.head(5)"
],
"execution_count": 44,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Restaurant</th>\n",
" <th>Location</th>\n",
" <th>Cuisines</th>\n",
" <th>Average_Cost</th>\n",
" <th>Minimum_Order</th>\n",
" <th>Rating</th>\n",
" <th>Votes</th>\n",
" <th>Reviews</th>\n",
" <th>Delivery_Time</th>\n",
" <th>Average_Cost_Cleaned</th>\n",
" <th>Minimum_Order_Cleaned</th>\n",
" <th>Rating_Cleaned</th>\n",
" <th>Votes_Cleaned</th>\n",
" <th>Reviews_Cleaned</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>ID_6321</td>\n",
" <td>FTI College, Law College Road, Pune</td>\n",
" <td>Fast Food, Rolls, Burger, Salad, Wraps</td>\n",
" <td>₹200</td>\n",
" <td>₹50</td>\n",
" <td>3.5</td>\n",
" <td>12</td>\n",
" <td>4</td>\n",
" <td>30 minutes</td>\n",
" <td>200</td>\n",
" <td>50</td>\n",
" <td>3.5</td>\n",
" <td>12</td>\n",
" <td>4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>ID_2882</td>\n",
" <td>Sector 3, Marathalli</td>\n",
" <td>Ice Cream, Desserts</td>\n",
" <td>₹100</td>\n",
" <td>₹50</td>\n",
" <td>3.5</td>\n",
" <td>11</td>\n",
" <td>4</td>\n",
" <td>30 minutes</td>\n",
" <td>100</td>\n",
" <td>50</td>\n",
" <td>3.5</td>\n",
" <td>11</td>\n",
" <td>4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>ID_1595</td>\n",
" <td>Mumbai Central</td>\n",
" <td>Italian, Street Food, Fast Food</td>\n",
" <td>₹150</td>\n",
" <td>₹50</td>\n",
" <td>3.6</td>\n",
" <td>99</td>\n",
" <td>30</td>\n",
" <td>65 minutes</td>\n",
" <td>150</td>\n",
" <td>50</td>\n",
" <td>3.6</td>\n",
" <td>99</td>\n",
" <td>30</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>ID_5929</td>\n",
" <td>Sector 1, Noida</td>\n",
" <td>Mughlai, North Indian, Chinese</td>\n",
" <td>₹250</td>\n",
" <td>₹99</td>\n",
" <td>3.7</td>\n",
" <td>176</td>\n",
" <td>95</td>\n",
" <td>30 minutes</td>\n",
" <td>250</td>\n",
" <td>99</td>\n",
" <td>3.7</td>\n",
" <td>176</td>\n",
" <td>95</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>ID_6123</td>\n",
" <td>Rmz Centennial, I Gate, Whitefield</td>\n",
" <td>Cafe, Beverages</td>\n",
" <td>₹200</td>\n",
" <td>₹99</td>\n",
" <td>3.2</td>\n",
" <td>521</td>\n",
" <td>235</td>\n",
" <td>65 minutes</td>\n",
" <td>200</td>\n",
" <td>99</td>\n",
" <td>3.2</td>\n",
" <td>521</td>\n",
" <td>235</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Restaurant ... Reviews_Cleaned\n",
"0 ID_6321 ... 4\n",
"1 ID_2882 ... 4\n",
"2 ID_1595 ... 30\n",
"3 ID_5929 ... 95\n",
"4 ID_6123 ... 235\n",
"\n",
"[5 rows x 14 columns]"
]
},
"metadata": {
"tags": []
},
"execution_count": 44
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "fbQIqpjpAZxP",
"colab_type": "code",
"outputId": "d46e3702-4075-4523-e376-6b7b8bce27f7",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 0
}
},
"source": [
"train.columns"
],
"execution_count": 45,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"Index(['Restaurant', 'Location', 'Cuisines', 'Average_Cost', 'Minimum_Order',\n",
" 'Rating', 'Votes', 'Reviews', 'Delivery_Time', 'Average_Cost_Cleaned',\n",
" 'Minimum_Order_Cleaned', 'Rating_Cleaned', 'Votes_Cleaned',\n",
" 'Reviews_Cleaned'],\n",
" dtype='object')"
]
},
"metadata": {
"tags": []
},
"execution_count": 45
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "WZvTDcx3Xgtc",
"colab_type": "text"
},
"source": [
"We are done with the cleaning part and now we will select only the columns we need for further stages. "
]
},
{
"cell_type": "code",
"metadata": {
"id": "3r-RUSjAAIgL",
"colab_type": "code",
"colab": {}
},
"source": [
"cols = ['Restaurant', 'Average_Cost_Cleaned',\n",
" 'Minimum_Order_Cleaned', 'Rating_Cleaned', 'Votes_Cleaned',\n",
" 'Reviews_Cleaned','Delivery_Time' ]"
],
"execution_count": 0,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "zdLJ2jsUAoCb",
"colab_type": "code",
"colab": {}
},
"source": [
"train_sample = train[cols]\n",
"test_sample = test[cols[:-1]]"
],
"execution_count": 0,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"id": "rRbkFzexX00x",
"colab_type": "text"
},
"source": [
"We will now merge all the cleaned features to form a perfect dataframe"
]
},
{
"cell_type": "code",
"metadata": {
"id": "2bYpHnkkBEOt",
"colab_type": "code",
"colab": {}
},
"source": [
"train_sample = pd.concat([pd.DataFrame(train_Location_splits), pd.DataFrame(train_Cuisines_splits),train_sample],sort=False,axis = 1)\n",
"test_sample = pd.concat([pd.DataFrame(test_Location_splits), pd.DataFrame(test_Cuisines_splits), test_sample],sort=False,axis = 1)"
],
"execution_count": 0,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "eBQh4JoFCEY-",
"colab_type": "code",
"outputId": "132e8027-b1b3-47e1-c9e6-544189fa1060",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 0
}
},
"source": [
"#lets take a look at the relation between the numeric features in the dataset\n",
"import seaborn\n",
"seaborn.pairplot(train_sample)"
],
"execution_count": 49,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"<seaborn.axisgrid.PairGrid at 0x7f6ac0cc8550>"
]
},
"metadata": {
"tags": []
},
"execution_count": 49
},
{
"output_type": "display_data",
"data": {
"image/png": 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zgdla69/2cd0EQaD7Ua6iMdN++IMkKUaGb3pz/e7RO3P9MjHpKUMxmqHQ+0RN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\nodVyXR5tMLWI6AXknRYUwgVgOoA5AL4C4E9Syn9NY9t6RGV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0zs5sgrJfezqd7Ekp/x+A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9sZeW7jD3XzOPVdsbrLZRrbwZ14z6FznQONat3370pUPcdslcfF6NKUUBFaMThxEzaPlr4Gwp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\ngqAB+V9x17jwkvSZ7wAAAABJRU5ErkJggg==\n",
"text/plain": [
"<Figure size 900x900 with 30 Axes>"
]
},
"metadata": {
"tags": []
}
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "RmXx7yZTGUs9",
"colab_type": "code",
"outputId": "f8154c2e-e19d-4ee1-a547-9a986cdb1bd8",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 0
}
},
"source": [
"seaborn.pairplot(test_sample)"
],
"execution_count": 50,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"<seaborn.axisgrid.PairGrid at 0x7f6aaf972e10>"
]
},
"metadata": {
"tags": []
},
"execution_count": 50
},
{
"output_type": "display_data",
"data": {
"image/png": 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C1aHqjHBAQThEmEnqOeMeCioIKoR4cvYsjfbY6IqFcXJGrbopso6o+uD4ybH54tZu57TH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1/Lmvu3D7+Bu1et5BxMddUbCuaMEVnxev1F2D78Rs64QWQnqs3xkbGI+AG8AuAKJBeAzwO4zu0C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bJt+JWpnrqglPqziGwnIjugB4M3KqU+N20UHOd66esrwEUiMhzdgaw07cJxwIXoQZBrReSP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\nAElFTkSuQmCC\n",
"text/plain": [
"<Figure size 900x900 with 30 Axes>"
]
},
"metadata": {
"tags": []
}
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "u1kumctM5Jf5",
"colab_type": "text"
},
"source": [
"##Data Preprocessing"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "1bxiaN83YEcA",
"colab_type": "text"
},
"source": [
"After cleaning, there are still some Data-Preparation tasks left. We still have some missing values that we allotted during the feature generation.\n",
"\n",
"In the Preprocessing stage we will perform the following :\n",
"\n",
"* Dealing with Nulls/empty cells\n",
"* Encoding Categorical variables\n",
"* Scaling the features"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "SCbszxL0-NPg",
"colab_type": "text"
},
"source": [
"### Removing Nulls"
]
},
{
"cell_type": "code",
"metadata": {
"id": "MkXVhYBP2EL4",
"colab_type": "code",
"outputId": "95e23890-f593-4dd4-f9a1-bb972c37538a",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 0
}
},
"source": [
"train_sample.isnull().sum()"
],
"execution_count": 51,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"Location_Feature_1 0\n",
"Location_Feature_2 1942\n",
"Location_Feature_3 6364\n",
"Location_Feature_4 10068\n",
"Cuisines_Feature_1 0\n",
"Cuisines_Feature_2 3012\n",
"Cuisines_Feature_3 6931\n",
"Cuisines_Feature_4 9393\n",
"Cuisines_Feature_5 10449\n",
"Cuisines_Feature_6 10854\n",
"Cuisines_Feature_7 10978\n",
"Cuisines_Feature_8 11046\n",
"Restaurant 0\n",
"Average_Cost_Cleaned 0\n",
"Minimum_Order_Cleaned 0\n",
"Rating_Cleaned 0\n",
"Votes_Cleaned 0\n",
"Reviews_Cleaned 0\n",
"Delivery_Time 0\n",
"dtype: int64"
]
},
"metadata": {
"tags": []
},
"execution_count": 51
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "xVejY3poZKSx",
"colab_type": "text"
},
"source": [
"Since the nulls are present only in the categorical features, in this approach, to make it simple I will just replace all the NaNs with a string 'NAN' and will use it as a added category or class."
]
},
{
"cell_type": "code",
"metadata": {
"id": "iQUda-wk2VvR",
"colab_type": "code",
"colab": {}
},
"source": [
"train_sample.fillna('NAN', inplace = True)"
],
"execution_count": 0,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "i11idxAi2gI0",
"colab_type": "code",
"outputId": "20e220e7-7736-48cb-f804-160e581bbd68",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 0
}
},
"source": [
"train_sample.isnull().sum()"
],
"execution_count": 53,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"Location_Feature_1 0\n",
"Location_Feature_2 0\n",
"Location_Feature_3 0\n",
"Location_Feature_4 0\n",
"Cuisines_Feature_1 0\n",
"Cuisines_Feature_2 0\n",
"Cuisines_Feature_3 0\n",
"Cuisines_Feature_4 0\n",
"Cuisines_Feature_5 0\n",
"Cuisines_Feature_6 0\n",
"Cuisines_Feature_7 0\n",
"Cuisines_Feature_8 0\n",
"Restaurant 0\n",
"Average_Cost_Cleaned 0\n",
"Minimum_Order_Cleaned 0\n",
"Rating_Cleaned 0\n",
"Votes_Cleaned 0\n",
"Reviews_Cleaned 0\n",
"Delivery_Time 0\n",
"dtype: int64"
]
},
"metadata": {
"tags": []
},
"execution_count": 53
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "b3UkAuNO2ntr",
"colab_type": "code",
"outputId": "884f2be3-75ce-4d50-a314-297288c6070b",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 0
}
},
"source": [
"train_sample.isnull().sum()"
],
"execution_count": 54,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"Location_Feature_1 0\n",
"Location_Feature_2 0\n",
"Location_Feature_3 0\n",
"Location_Feature_4 0\n",
"Cuisines_Feature_1 0\n",
"Cuisines_Feature_2 0\n",
"Cuisines_Feature_3 0\n",
"Cuisines_Feature_4 0\n",
"Cuisines_Feature_5 0\n",
"Cuisines_Feature_6 0\n",
"Cuisines_Feature_7 0\n",
"Cuisines_Feature_8 0\n",
"Restaurant 0\n",
"Average_Cost_Cleaned 0\n",
"Minimum_Order_Cleaned 0\n",
"Rating_Cleaned 0\n",
"Votes_Cleaned 0\n",
"Reviews_Cleaned 0\n",
"Delivery_Time 0\n",
"dtype: int64"
]
},
"metadata": {
"tags": []
},
"execution_count": 54
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "7ePhAXn12iBg",
"colab_type": "code",
"colab": {}
},
"source": [
"test_sample.fillna('NAN', inplace = True)"
],
"execution_count": 0,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "ddDN2nVH2h_S",
"colab_type": "code",
"outputId": "eb8beb8d-997e-4330-c910-8b73e86db5f0",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 0
}
},
"source": [
"train_sample.isnull().sum()"
],
"execution_count": 56,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"Location_Feature_1 0\n",
"Location_Feature_2 0\n",
"Location_Feature_3 0\n",
"Location_Feature_4 0\n",
"Cuisines_Feature_1 0\n",
"Cuisines_Feature_2 0\n",
"Cuisines_Feature_3 0\n",
"Cuisines_Feature_4 0\n",
"Cuisines_Feature_5 0\n",
"Cuisines_Feature_6 0\n",
"Cuisines_Feature_7 0\n",
"Cuisines_Feature_8 0\n",
"Restaurant 0\n",
"Average_Cost_Cleaned 0\n",
"Minimum_Order_Cleaned 0\n",
"Rating_Cleaned 0\n",
"Votes_Cleaned 0\n",
"Reviews_Cleaned 0\n",
"Delivery_Time 0\n",
"dtype: int64"
]
},
"metadata": {
"tags": []
},
"execution_count": 56
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "wRVErMt0HBFp",
"colab_type": "text"
},
"source": [
"### Encoding Categories"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "qkuN0nZ8ZcMo",
"colab_type": "text"
},
"source": [
"\n",
"\n",
"---\n",
"\n",
"Here we will use a simple Label Encoder to transform all the strings or categories."
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "tQXtDXaONwjx",
"colab_type": "text"
},
"source": [
"####Locations & Cuisines"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "PWKUUgjRZ3oO",
"colab_type": "text"
},
"source": [
"We will first find the unique values or categories in each of the categorical features and fit the label encoder with the unique values. The encoder eill assign an integer code to each of the categories which can be used to tranform the entire categorical feature column."
]
},
{
"cell_type": "code",
"metadata": {
"id": "3C5FxHrpJyy1",
"colab_type": "code",
"colab": {}
},
"source": [
"temp1 = []\n",
"for i in train_Cuisines_splits.keys():\n",
" for j in train_Cuisines_splits.get(i):\n",
" temp1.append(j)\n",
"\n",
"temp2 = []\n",
"for i in test_Cuisines_splits.keys():\n",
" for j in test_Cuisines_splits.get(i):\n",
" temp2.append(j)\n",
"\n",
"temp1.extend(temp2)\n",
"\n",
"unique_cuisines = list(pd.Series(temp1).unique())\n",
"unique_cuisines.append('NAN')"
],
"execution_count": 0,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"id": "-TwuUkxbafu2",
"colab_type": "text"
},
"source": [
"**Note:**\n",
"We are also adding an extra category called NAN that we used to replace the NaN values."
]
},
{
"cell_type": "code",
"metadata": {
"id": "zVFV3HlYcnB8",
"colab_type": "code",
"outputId": "0c327500-2465-47ec-98ac-57826b47d5e4",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 0
}
},
"source": [
"len(unique_cuisines)"
],
"execution_count": 58,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"103"
]
},
"metadata": {
"tags": []
},
"execution_count": 58
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "sXmPQgxIayR2",
"colab_type": "text"
},
"source": [
"The location is a tricky feature. You can use external data to fill the missing data. For example, if the city is missing you can use the street name and try to find the City for the missing fields.\n",
"\n",
"For simplicity, here I have followed the same approach as I did for Cuisines which is not a perfect solution."
]
},
{
"cell_type": "code",
"metadata": {
"id": "PFdxEMFYapOg",
"colab_type": "code",
"colab": {}
},
"source": [
"temp1 = []\n",
"for i in train_Location_splits.keys():\n",
" for j in train_Location_splits.get(i):\n",
" temp1.append(j)\n",
"\n",
"temp2 = []\n",
"for i in test_Location_splits.keys():\n",
" for j in test_Location_splits.get(i):\n",
" temp2.append(j)\n",
"\n",
"temp1.extend(temp2)\n",
"\n",
"unique_locations = list(pd.Series(temp1).unique())\n",
"unique_locations.append('NAN')"
],
"execution_count": 0,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "NgYB9IGpao_8",
"colab_type": "code",
"outputId": "425163b6-f6bd-4eec-e807-f11c0da0fca8",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 0
}
},
"source": [
"len(unique_locations)"
],
"execution_count": 60,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"67"
]
},
"metadata": {
"tags": []
},
"execution_count": 60
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "y4VLRR_SXPeM",
"colab_type": "code",
"colab": {}
},
"source": [
"#encoding the categorical Features\n",
"from sklearn.preprocessing import LabelEncoder\n",
"le_c = LabelEncoder().fit(unique_cuisines)\n",
"le_l = LabelEncoder().fit(unique_locations)"
],
"execution_count": 0,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "LRZw0YSE3dYe",
"colab_type": "code",
"colab": {}
},
"source": [
"for i in train_Location_splits.keys():\n",
" train_sample[i] = le_l.transform(train_sample[i])"
],
"execution_count": 0,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "4U5u_YD63dVs",
"colab_type": "code",
"colab": {}
},
"source": [
"\n",
"for i in train_Cuisines_splits.keys():\n",
" train_sample[i] = le_c.transform(train_sample[i])"
],
"execution_count": 0,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "Fj2Jj6BW3fuL",
"colab_type": "code",
"colab": {}
},
"source": [
"for i in train_Location_splits.keys():\n",
" test_sample[i] = le_l.transform(test_sample[i])"
],
"execution_count": 0,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "o1syc4_-3fsG",
"colab_type": "code",
"colab": {}
},
"source": [
"for i in test_Cuisines_splits.keys():\n",
" test_sample[i] = le_c.transform(test_sample[i])"
],
"execution_count": 0,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"id": "1R2AgdQg5OKR",
"colab_type": "text"
},
"source": [
"####Restaurant IDs"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "DAY-qFUkbvOn",
"colab_type": "text"
},
"source": [
"We will follow a similar approach for encoding the Restaurant IDs"
]
},
{
"cell_type": "code",
"metadata": {
"id": "Gjy67q7m4yyI",
"colab_type": "code",
"colab": {}
},
"source": [
"t1 = list(train_sample['Restaurant'])\n",
"t2 = list(test_sample['Restaurant'])\n",
"\n",
"t1.extend(t2)\n",
"unique_ids = list(set(t1))"
],
"execution_count": 0,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "rMEP1sK351oP",
"colab_type": "code",
"outputId": "178c18d4-09fb-49fe-ec90-8973008dd8fd",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 0
}
},
"source": [
"len(unique_ids)"
],
"execution_count": 67,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"8661"
]
},
"metadata": {
"tags": []
},
"execution_count": 67
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "MPZ8Vu2q3foQ",
"colab_type": "code",
"colab": {}
},
"source": [
"le_id = LabelEncoder().fit(unique_ids)"
],
"execution_count": 0,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "OkIo4_xX3fmb",
"colab_type": "code",
"colab": {}
},
"source": [
"train_sample['Restaurant'] = le_id.transform(train_sample['Restaurant'])\n",
"test_sample['Restaurant'] = le_id.transform(test_sample['Restaurant'])"
],
"execution_count": 0,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"id": "FIGYqSTs6siA",
"colab_type": "text"
},
"source": [
"### Scaling"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "RLNrIonDb77p",
"colab_type": "text"
},
"source": [
"We will now normalize the data using the StandardScaler"
]
},
{
"cell_type": "code",
"metadata": {
"id": "BapushLu67t_",
"colab_type": "code",
"colab": {}
},
"source": [
"cols = list(train_sample.columns)"
],
"execution_count": 0,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "iN9VLzktXPZV",
"colab_type": "code",
"colab": {}
},
"source": [
"from sklearn.preprocessing import StandardScaler\n",
"ss = StandardScaler()"
],
"execution_count": 0,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "FnZPES32XPQX",
"colab_type": "code",
"colab": {}
},
"source": [
"train_sample[cols[:-1]] = ss.fit_transform(train_sample[cols[:-1]])"
],
"execution_count": 0,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "usUErPxy7arJ",
"colab_type": "code",
"colab": {}
},
"source": [
"test_sample[cols[:-1]] = ss.fit_transform(test_sample[cols[:-1]])"
],
"execution_count": 0,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "SeC25GVmgItl",
"colab_type": "code",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 0
},
"outputId": "24fd1a9a-ba32-466c-ac7a-2f354e5b64f1"
},
"source": [
"train_sample.head()"
],
"execution_count": 74,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Location_Feature_1</th>\n",
" <th>Location_Feature_2</th>\n",
" <th>Location_Feature_3</th>\n",
" <th>Location_Feature_4</th>\n",
" <th>Cuisines_Feature_1</th>\n",
" <th>Cuisines_Feature_2</th>\n",
" <th>Cuisines_Feature_3</th>\n",
" <th>Cuisines_Feature_4</th>\n",
" <th>Cuisines_Feature_5</th>\n",
" <th>Cuisines_Feature_6</th>\n",
" <th>Cuisines_Feature_7</th>\n",
" <th>Cuisines_Feature_8</th>\n",
" <th>Restaurant</th>\n",
" <th>Average_Cost_Cleaned</th>\n",
" <th>Minimum_Order_Cleaned</th>\n",
" <th>Rating_Cleaned</th>\n",
" <th>Votes_Cleaned</th>\n",
" <th>Reviews_Cleaned</th>\n",
" <th>Delivery_Time</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>-0.867166</td>\n",
" <td>-0.408678</td>\n",
" <td>1.683093</td>\n",
" <td>-0.161374</td>\n",
" <td>-0.714599</td>\n",
" <td>1.656380</td>\n",
" <td>0.137213</td>\n",
" <td>3.865914</td>\n",
" <td>7.460013</td>\n",
" <td>-0.125106</td>\n",
" <td>-0.08579</td>\n",
" <td>-0.057042</td>\n",
" <td>0.625956</td>\n",
" <td>-0.020864</td>\n",
" <td>-0.180293</td>\n",
" <td>-0.301852</td>\n",
" <td>-0.464423</td>\n",
" <td>-0.417345</td>\n",
" <td>30 minutes</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>1.246323</td>\n",
" <td>-0.117669</td>\n",
" <td>-0.660441</td>\n",
" <td>-0.161374</td>\n",
" <td>-0.312564</td>\n",
" <td>-0.125757</td>\n",
" <td>-0.621557</td>\n",
" <td>-0.359765</td>\n",
" <td>-0.205249</td>\n",
" <td>-0.125106</td>\n",
" <td>-0.08579</td>\n",
" <td>-0.057042</td>\n",
" <td>-0.897145</td>\n",
" <td>-0.791117</td>\n",
" <td>-0.180293</td>\n",
" <td>-0.301852</td>\n",
" <td>-0.466421</td>\n",
" <td>-0.417345</td>\n",
" <td>30 minutes</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>0.242416</td>\n",
" <td>-1.669717</td>\n",
" <td>-0.660441</td>\n",
" <td>-0.161374</td>\n",
" <td>-0.129821</td>\n",
" <td>1.953402</td>\n",
" <td>0.534663</td>\n",
" <td>-0.359765</td>\n",
" <td>-0.205249</td>\n",
" <td>-0.125106</td>\n",
" <td>-0.08579</td>\n",
" <td>-0.057042</td>\n",
" <td>-1.467013</td>\n",
" <td>-0.405990</td>\n",
" <td>-0.180293</td>\n",
" <td>-0.035808</td>\n",
" <td>-0.290596</td>\n",
" <td>-0.326311</td>\n",
" <td>65 minutes</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>1.140649</td>\n",
" <td>0.415848</td>\n",
" <td>-0.660441</td>\n",
" <td>-0.161374</td>\n",
" <td>0.637700</td>\n",
" <td>1.326354</td>\n",
" <td>0.354004</td>\n",
" <td>-0.359765</td>\n",
" <td>-0.205249</td>\n",
" <td>-0.125106</td>\n",
" <td>-0.08579</td>\n",
" <td>-0.057042</td>\n",
" <td>0.451808</td>\n",
" <td>0.364263</td>\n",
" <td>2.461158</td>\n",
" <td>0.230235</td>\n",
" <td>-0.136749</td>\n",
" <td>-0.098724</td>\n",
" <td>30 minutes</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>0.982137</td>\n",
" <td>-0.602684</td>\n",
" <td>2.580618</td>\n",
" <td>-0.161374</td>\n",
" <td>-1.043537</td>\n",
" <td>-0.620795</td>\n",
" <td>-0.621557</td>\n",
" <td>-0.359765</td>\n",
" <td>-0.205249</td>\n",
" <td>-0.125106</td>\n",
" <td>-0.08579</td>\n",
" <td>-0.057042</td>\n",
" <td>0.538284</td>\n",
" <td>-0.020864</td>\n",
" <td>2.461158</td>\n",
" <td>-1.099981</td>\n",
" <td>0.552564</td>\n",
" <td>0.391462</td>\n",
" <td>65 minutes</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Location_Feature_1 Location_Feature_2 ... Reviews_Cleaned Delivery_Time\n",
"0 -0.867166 -0.408678 ... -0.417345 30 minutes\n",
"1 1.246323 -0.117669 ... -0.417345 30 minutes\n",
"2 0.242416 -1.669717 ... -0.326311 65 minutes\n",
"3 1.140649 0.415848 ... -0.098724 30 minutes\n",
"4 0.982137 -0.602684 ... 0.391462 65 minutes\n",
"\n",
"[5 rows x 19 columns]"
]
},
"metadata": {
"tags": []
},
"execution_count": 74
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "mF9OMgtMgKRt",
"colab_type": "code",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 0
},
"outputId": "057cce93-9053-4a25-8ef8-11ee68e02b6a"
},
"source": [
"test_sample.head()"
],
"execution_count": 75,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Location_Feature_1</th>\n",
" <th>Location_Feature_2</th>\n",
" <th>Location_Feature_3</th>\n",
" <th>Location_Feature_4</th>\n",
" <th>Cuisines_Feature_1</th>\n",
" <th>Cuisines_Feature_2</th>\n",
" <th>Cuisines_Feature_3</th>\n",
" <th>Cuisines_Feature_4</th>\n",
" <th>Cuisines_Feature_5</th>\n",
" <th>Cuisines_Feature_6</th>\n",
" <th>Cuisines_Feature_7</th>\n",
" <th>Cuisines_Feature_8</th>\n",
" <th>Restaurant</th>\n",
" <th>Average_Cost_Cleaned</th>\n",
" <th>Minimum_Order_Cleaned</th>\n",
" <th>Rating_Cleaned</th>\n",
" <th>Votes_Cleaned</th>\n",
" <th>Reviews_Cleaned</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>0.083713</td>\n",
" <td>1.243494</td>\n",
" <td>-0.479103</td>\n",
" <td>0.871523</td>\n",
" <td>0.809765</td>\n",
" <td>-0.204497</td>\n",
" <td>-0.346423</td>\n",
" <td>-0.351099</td>\n",
" <td>-0.196822</td>\n",
" <td>-0.12173</td>\n",
" <td>-0.082445</td>\n",
" <td>-0.042516</td>\n",
" <td>-0.897086</td>\n",
" <td>1.350471</td>\n",
" <td>-0.177278</td>\n",
" <td>1.612406</td>\n",
" <td>0.276837</td>\n",
" <td>0.428327</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>0.083713</td>\n",
" <td>1.243494</td>\n",
" <td>-0.479103</td>\n",
" <td>0.871523</td>\n",
" <td>-1.302905</td>\n",
" <td>0.628995</td>\n",
" <td>-0.605296</td>\n",
" <td>-0.351099</td>\n",
" <td>-0.196822</td>\n",
" <td>-0.12173</td>\n",
" <td>-0.082445</td>\n",
" <td>-0.042516</td>\n",
" <td>1.096904</td>\n",
" <td>-0.833142</td>\n",
" <td>-0.177278</td>\n",
" <td>0.029586</td>\n",
" <td>0.029087</td>\n",
" <td>0.035125</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>1.143599</td>\n",
" <td>0.416451</td>\n",
" <td>-0.673221</td>\n",
" <td>-0.151585</td>\n",
" <td>-0.746940</td>\n",
" <td>-1.104668</td>\n",
" <td>-0.605296</td>\n",
" <td>-0.351099</td>\n",
" <td>-0.196822</td>\n",
" <td>-0.12173</td>\n",
" <td>-0.082445</td>\n",
" <td>-0.042516</td>\n",
" <td>-0.101583</td>\n",
" <td>-0.833142</td>\n",
" <td>-0.177278</td>\n",
" <td>-0.006736</td>\n",
" <td>-0.411358</td>\n",
" <td>-0.377352</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>-1.665097</td>\n",
" <td>0.367801</td>\n",
" <td>-0.236455</td>\n",
" <td>-0.151585</td>\n",
" <td>0.513250</td>\n",
" <td>1.362467</td>\n",
" <td>0.393217</td>\n",
" <td>1.259565</td>\n",
" <td>7.241203</td>\n",
" <td>-0.12173</td>\n",
" <td>-0.082445</td>\n",
" <td>-0.042516</td>\n",
" <td>0.278454</td>\n",
" <td>0.040303</td>\n",
" <td>-0.177278</td>\n",
" <td>-0.006736</td>\n",
" <td>-0.347833</td>\n",
" <td>-0.311818</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>1.143599</td>\n",
" <td>0.416451</td>\n",
" <td>-0.673221</td>\n",
" <td>-0.151585</td>\n",
" <td>-0.932262</td>\n",
" <td>-0.037799</td>\n",
" <td>-0.605296</td>\n",
" <td>-0.351099</td>\n",
" <td>-0.196822</td>\n",
" <td>-0.12173</td>\n",
" <td>-0.082445</td>\n",
" <td>-0.042516</td>\n",
" <td>-0.715119</td>\n",
" <td>-0.396419</td>\n",
" <td>-0.177278</td>\n",
" <td>-1.895735</td>\n",
" <td>-0.407123</td>\n",
" <td>-0.385062</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Location_Feature_1 Location_Feature_2 ... Votes_Cleaned Reviews_Cleaned\n",
"0 0.083713 1.243494 ... 0.276837 0.428327\n",
"1 0.083713 1.243494 ... 0.029087 0.035125\n",
"2 1.143599 0.416451 ... -0.411358 -0.377352\n",
"3 -1.665097 0.367801 ... -0.347833 -0.311818\n",
"4 1.143599 0.416451 ... -0.407123 -0.385062\n",
"\n",
"[5 rows x 18 columns]"
]
},
"metadata": {
"tags": []
},
"execution_count": 75
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "cVLi3tWF77GQ",
"colab_type": "text"
},
"source": [
"##Modeling"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "r7mSn15PcLBy",
"colab_type": "text"
},
"source": [
"Finally, we are ready for modeling. We will split the training set into training and validation sets.\n",
"\n",
"We will then use the training set to train and validation set to test the performance of the model.\n",
"\n",
"Finally we will use the given test set for predicting.\n",
"\n",
"\n"
]
},
{
"cell_type": "code",
"metadata": {
"id": "GyvnZnyAcmTF",
"colab_type": "code",
"colab": {}
},
"source": [
"from sklearn.model_selection import train_test_split\n",
"\n",
"train, val = train_test_split(train_sample, test_size = 0.1, random_state = 123)"
],
"execution_count": 0,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "moR5oT7H8Gj2",
"colab_type": "code",
"colab": {}
},
"source": [
"X_train = train[cols[:-1]]\n",
"Y_train = train[cols[-1]]\n",
"\n",
"X_Val = val[cols[:-1]]\n",
"Y_Val = val[cols[-1]]\n",
"\n",
"X_test = test_sample[cols[:-1]]"
],
"execution_count": 0,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "nsciW_cU7ak5",
"colab_type": "code",
"outputId": "92e4f2ca-8a91-48f3-bcfe-ae83b64cc6c5",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 136
}
},
"source": [
"from xgboost import XGBClassifier\n",
"\n",
"xgb = XGBClassifier()\n",
"\n",
"xgb.fit(X_train,Y_train)"
],
"execution_count": 78,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"XGBClassifier(base_score=0.5, booster='gbtree', colsample_bylevel=1,\n",
" colsample_bynode=1, colsample_bytree=1, gamma=0,\n",
" learning_rate=0.1, max_delta_step=0, max_depth=3,\n",
" min_child_weight=1, missing=None, n_estimators=100, n_jobs=1,\n",
" nthread=None, objective='multi:softprob', random_state=0,\n",
" reg_alpha=0, reg_lambda=1, scale_pos_weight=1, seed=None,\n",
" silent=None, subsample=1, verbosity=1)"
]
},
"metadata": {
"tags": []
},
"execution_count": 78
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "0hBSBM_y76WT",
"colab_type": "code",
"outputId": "3f6a8a7d-70e7-4a8d-e526-3888d8848fdf",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 34
}
},
"source": [
"xgb.score(X_Val,Y_Val)"
],
"execution_count": 79,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"0.7216216216216216"
]
},
"metadata": {
"tags": []
},
"execution_count": 79
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "mWJ9EMx7dpTY",
"colab_type": "code",
"colab": {}
},
"source": [
"Predictions = xgb.predict(X_test)"
],
"execution_count": 0,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"id": "2d70kfWweZ92",
"colab_type": "text"
},
"source": [
"Lets write the predictions into an excel file for uploading into MachineHack"
]
},
{
"cell_type": "code",
"metadata": {
"id": "r4K25dPrdke5",
"colab_type": "code",
"colab": {}
},
"source": [
"pd.DataFrame(Predictions, columns = ['Delivery_Time']).to_excel(\"/GD/My Drive/Colab Notebooks/Food_delivery_Time_Prediction/Submission_1.xlsx\", index = False)"
],
"execution_count": 0,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"id": "fJ6fmF5FiDGi",
"colab_type": "text"
},
"source": [
"Vola ! You now a solution. Upload the file at MachinHack to know your score!\n",
"\n",
"---\n",
"\n",
"This is a baseline model. Fine tuning the model can give better results. Also, tryout different algorithms to find the best."
]
}
]
}
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