new-york-taxi-fare-prediction.ipynb

{"metadata":{"kernelspec":{"name":"python3","display_name":"Python 3","language":"python"},"language_info":{"name":"python","version":"3.10.10","mimetype":"text/x-python","codemirror_mode":{"name":"ipython","version":3},"pygments_lexer":"ipython3","nbconvert_exporter":"python","file_extension":".py"}},"nbformat_minor":5,"nbformat":4,"cells":[{"cell_type":"markdown","source":"# New York Taxi Fare Prediction Challenge $Best Score: 3.1899$","metadata":{}},{"cell_type":"markdown","source":"The New York City Taxi Fare Prediction competition on Kaggle is a machine learning competition that challenges participants to develop predictive models for estimating the fare amount of taxi rides in New York City. The goal of this competition is to create accurate and reliable models that can accurately predict the fare amount based on various input features.\n\nThe competition provides participants with a dataset containing information about taxi rides, including features such as pickup and dropoff locations, timestamps, passenger counts, and other relevant details. The dataset is based on real-world taxi ride data from New York City.\n\nThe objective of the competition is to use this dataset to build a machine learning model that can accurately predict the fare amount for a given taxi ride. Participants are encouraged to explore the dataset, preprocess the data, and apply various machine learning techniques to develop their models.\n\nThe evaluation metric for this competition is the root mean squared error (RMSE) between the predicted fare amount and the actual fare amount. Lower values of RMSE indicate better performance, meaning that the model's predictions are closer to the true fare amounts.\n\nParticipants are required to submit their predictions for a test set provided by the competition. The competition leaderboard is based on the RMSE score achieved on the test set, and the top-performing participants are eligible for prizes.\n\nThis competition offers a practical and challenging problem in the field of predictive modeling. It requires participants to utilize their skills in data preprocessing, feature engineering, and machine learning algorithms to develop models that can accurately predict taxi fare amounts in a real-world scenario. It provides an opportunity to showcase creativity and analytical thinking while also learning from the diverse approaches and techniques employed by other participants.","metadata":{}},{"cell_type":"markdown","source":"# Importing necessary libraries and adjusting aesthetics for plots","metadata":{}},{"cell_type":"code","source":"#i'm Gonna import just the basic libraies necessary to start the analysis. Then i'll import whatever i need on the go\nimport pandas as pd\nimport numpy as np\npd.set_option(\"display.max_columns\", 120) #to remove the limit of columns shows as output\n\n# Importing Necessary libraies for viz\nimport matplotlib\nimport matplotlib.pyplot as plt\nimport seaborn as sns\n%matplotlib inline\n\n\n# This is just some asthetics settings for plots before we start EDA\npd.set_option('display.max_columns', None) #Enable to show max columns in code cells\nsns.set_style('darkgrid') #set sns plot background\nmatplotlib.rcParams['font.size'] = 14 #set the deafult plot font size for this notebook\nmatplotlib.rcParams['figure.figsize'] = (10, 6) #set the deafult plot size for this notebook\nmatplotlib.rcParams['figure.facecolor'] = '#00000000' #set matplotlib plot background","metadata":{"execution":{"iopub.status.busy":"2023-05-19T08:17:19.972787Z","iopub.execute_input":"2023-05-19T08:17:19.973696Z","iopub.status.idle":"2023-05-19T08:17:21.114171Z","shell.execute_reply.started":"2023-05-19T08:17:19.973639Z","shell.execute_reply":"2023-05-19T08:17:21.112529Z"},"trusted":true},"execution_count":1,"outputs":[{"name":"stderr","text":"/opt/conda/lib/python3.10/site-packages/scipy/__init__.py:146: UserWarning: A NumPy version >=1.16.5 and <1.23.0 is required for this version of SciPy (detected version 1.23.5\n  warnings.warn(f\"A NumPy version >={np_minversion} and <{np_maxversion}\"\n","output_type":"stream"}]},{"cell_type":"markdown","source":"# Sample Selection for training","metadata":{}},{"cell_type":"code","source":"sample_size=0.1 #i'm taking a sample size of 10%\n\nimport random\nrandom.seed(123)\n\ndtypes = {\n    'fare_amount': 'float32',\n    'pickup_longitude': 'float32',\n    'pickup_latitude': 'float32',\n    'dropoff_longitude': 'float32',\n    'passenger_count': 'float32'\n}\n\ntrain = pd.read_csv(\n         \"/kaggle/input/new-york-city-taxi-fare-prediction/train.csv\", \n         skiprows=lambda i: i>0 and random.random() > sample_size,dtype=dtypes\n)\n#This function is simple and effective to apply.\n# you can find this code here (https://stackoverflow.com/a/48589768)\nprint(\"before dropping NA:{}\".format(train.isna().sum()))\ntrain=train.dropna()                                                            # Dropping NA\nprint(\"before dropping NA:{}\".format(train.isna().sum()))\ntrain.describe() ","metadata":{"execution":{"iopub.status.busy":"2023-05-19T08:18:16.404079Z","iopub.execute_input":"2023-05-19T08:18:16.404439Z","iopub.status.idle":"2023-05-19T08:20:43.561477Z","shell.execute_reply.started":"2023-05-19T08:18:16.404408Z","shell.execute_reply":"2023-05-19T08:20:43.560512Z"},"trusted":true},"execution_count":3,"outputs":[{"name":"stdout","text":"before dropping NA:key                   0\nfare_amount           0\npickup_datetime       0\npickup_longitude      0\npickup_latitude       0\ndropoff_longitude    35\ndropoff_latitude     35\npassenger_count       0\ndtype: int64\nbefore dropping NA:key                  0\nfare_amount          0\npickup_datetime      0\npickup_longitude     0\npickup_latitude      0\ndropoff_longitude    0\ndropoff_latitude     0\npassenger_count      0\ndtype: int64\n","output_type":"stream"},{"execution_count":3,"output_type":"execute_result","data":{"text/plain":"        fare_amount  pickup_longitude  pickup_latitude  dropoff_longitude  \\\ncount  5.539356e+06      5.539356e+06     5.539356e+06       5.539356e+06   \nmean   1.133880e+01     -7.251791e+01     3.991663e+01      -7.251720e+01   \nstd    9.789072e+00      1.282210e+01     9.272609e+00       1.293722e+01   \nmin   -9.130000e+01     -3.309275e+03    -3.429363e+03      -3.442025e+03   \n25%    6.000000e+00     -7.399207e+01     4.073495e+01      -7.399139e+01   \n50%    8.500000e+00     -7.398180e+01     4.075267e+01      -7.398016e+01   \n75%    1.250000e+01     -7.396708e+01     4.076714e+01      -7.396368e+01   \nmax    1.097480e+03      3.456223e+03     3.375234e+03       3.456223e+03   \n\n       dropoff_latitude  passenger_count  \ncount      5.539356e+06     5.539356e+06  \nmean       3.992263e+01     1.686382e+00  \nstd        9.337818e+00     1.332785e+00  \nmin       -3.429363e+03     0.000000e+00  \n25%        4.073407e+01     1.000000e+00  \n50%        4.075317e+01     1.000000e+00  \n75%        4.076811e+01     2.000000e+00  \nmax        3.390614e+03     2.080000e+02  ","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>fare_amount</th>\n      <th>pickup_longitude</th>\n      <th>pickup_latitude</th>\n      <th>dropoff_longitude</th>\n      <th>dropoff_latitude</th>\n      <th>passenger_count</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>count</th>\n      <td>5.539356e+06</td>\n      <td>5.539356e+06</td>\n      <td>5.539356e+06</td>\n      <td>5.539356e+06</td>\n      <td>5.539356e+06</td>\n      <td>5.539356e+06</td>\n    </tr>\n    <tr>\n      <th>mean</th>\n      <td>1.133880e+01</td>\n      <td>-7.251791e+01</td>\n      <td>3.991663e+01</td>\n      <td>-7.251720e+01</td>\n      <td>3.992263e+01</td>\n      <td>1.686382e+00</td>\n    </tr>\n    <tr>\n      <th>std</th>\n      <td>9.789072e+00</td>\n      <td>1.282210e+01</td>\n      <td>9.272609e+00</td>\n      <td>1.293722e+01</td>\n      <td>9.337818e+00</td>\n      <td>1.332785e+00</td>\n    </tr>\n    <tr>\n      <th>min</th>\n      <td>-9.130000e+01</td>\n      <td>-3.309275e+03</td>\n      <td>-3.429363e+03</td>\n      <td>-3.442025e+03</td>\n      <td>-3.429363e+03</td>\n      <td>0.000000e+00</td>\n    </tr>\n    <tr>\n      <th>25%</th>\n      <td>6.000000e+00</td>\n      <td>-7.399207e+01</td>\n      <td>4.073495e+01</td>\n      <td>-7.399139e+01</td>\n      <td>4.073407e+01</td>\n      <td>1.000000e+00</td>\n    </tr>\n    <tr>\n      <th>50%</th>\n      <td>8.500000e+00</td>\n      <td>-7.398180e+01</td>\n      <td>4.075267e+01</td>\n      <td>-7.398016e+01</td>\n      <td>4.075317e+01</td>\n      <td>1.000000e+00</td>\n    </tr>\n    <tr>\n      <th>75%</th>\n      <td>1.250000e+01</td>\n      <td>-7.396708e+01</td>\n      <td>4.076714e+01</td>\n      <td>-7.396368e+01</td>\n      <td>4.076811e+01</td>\n      <td>2.000000e+00</td>\n    </tr>\n    <tr>\n      <th>max</th>\n      <td>1.097480e+03</td>\n      <td>3.456223e+03</td>\n      <td>3.375234e+03</td>\n      <td>3.456223e+03</td>\n      <td>3.390614e+03</td>\n      <td>2.080000e+02</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}]},{"cell_type":"markdown","source":"We are working with a dataset containing information about New York City taxi fares. The goal is to build a machine learning model to predict the fare amount based on various features.\n\nFirst, we import the necessary libraries, including pandas for data manipulation and random for random number generation. We set a seed value (123) for reproducibility.\n\nNext, we define the variable `sample_size` to represent the percentage of the dataset we want to use for training. Here, we are taking a sample size of 10% (0.1).\n\nThe `dtypes` dictionary specifies the desired data types for each column in the dataset. This helps optimize memory usage by reducing the memory footprint of the loaded data.\n\nWe then use the `pd.read_csv()` function to read the CSV file containing the dataset. We pass a custom `skiprows` function that randomly selects rows to include in the sample based on the `sample_size` value. This allows us to efficiently load a smaller subset of the data.\n\nAfter loading the dataset, we print the number of missing values (NA) in each column before dropping them. This gives us an initial understanding of the data quality.\n\nTo handle missing values, we use the `train.dropna()` function, which removes rows containing any NA values. This ensures that our model training is based on clean data.\n\nFinally, we use the `train.describe()` function to generate descriptive statistics of the dataset, such as count, mean, standard deviation, and quartiles. This provides insights into the distribution and range of the numerical features.","metadata":{}},{"cell_type":"code","source":"test=pd.read_csv(\"/kaggle/input/new-york-city-taxi-fare-prediction/test.csv\")\nprint(\"before dropping NA:{}\".format(train.isna().sum()))\ntest=test.dropna()                                    # Dropping NA\nprint(\"before dropping NA:{}\".format(train.isna().sum()))\ntest.describe()","metadata":{},"execution_count":4,"outputs":[{"name":"stdout","output_type":"stream","text":"before dropping NA:key                  0\n\nfare_amount          0\n\npickup_datetime      0\n\npickup_longitude     0\n\npickup_latitude      0\n\ndropoff_longitude    0\n\ndropoff_latitude     0\n\npassenger_count      0\n\ndtype: int64\n\nbefore dropping NA:key                  0\n\nfare_amount          0\n\npickup_datetime      0\n\npickup_longitude     0\n\npickup_latitude      0\n\ndropoff_longitude    0\n\ndropoff_latitude     0\n\npassenger_count      0\n\ndtype: int64\n"},{"execution_count":4,"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>pickup_longitude</th>\n","      <th>pickup_latitude</th>\n","      <th>dropoff_longitude</th>\n","      <th>dropoff_latitude</th>\n","      <th>passenger_count</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>count</th>\n","      <td>9914.000000</td>\n","      <td>9914.000000</td>\n","      <td>9914.000000</td>\n","      <td>9914.000000</td>\n","      <td>9914.000000</td>\n","    </tr>\n","    <tr>\n","      <th>mean</th>\n","      <td>-73.974722</td>\n","      <td>40.751041</td>\n","      <td>-73.973657</td>\n","      <td>40.751743</td>\n","      <td>1.671273</td>\n","    </tr>\n","    <tr>\n","      <th>std</th>\n","      <td>0.042774</td>\n","      <td>0.033541</td>\n","      <td>0.039072</td>\n","      <td>0.035435</td>\n","      <td>1.278747</td>\n","    </tr>\n","    <tr>\n","      <th>min</th>\n","      <td>-74.252193</td>\n","      <td>40.573143</td>\n","      <td>-74.263242</td>\n","      <td>40.568973</td>\n","      <td>1.000000</td>\n","    </tr>\n","    <tr>\n","      <th>25%</th>\n","      <td>-73.992501</td>\n","      <td>40.736125</td>\n","      <td>-73.991247</td>\n","      <td>40.735254</td>\n","      <td>1.000000</td>\n","    </tr>\n","    <tr>\n","      <th>50%</th>\n","      <td>-73.982326</td>\n","      <td>40.753051</td>\n","      <td>-73.980015</td>\n","      <td>40.754065</td>\n","      <td>1.000000</td>\n","    </tr>\n","    <tr>\n","      <th>75%</th>\n","      <td>-73.968013</td>\n","      <td>40.767113</td>\n","      <td>-73.964059</td>\n","      <td>40.768757</td>\n","      <td>2.000000</td>\n","    </tr>\n","    <tr>\n","      <th>max</th>\n","      <td>-72.986532</td>\n","      <td>41.709555</td>\n","      <td>-72.990963</td>\n","      <td>41.696683</td>\n","      <td>6.000000</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["       pickup_longitude  pickup_latitude  dropoff_longitude  dropoff_latitude   \n","count       9914.000000      9914.000000        9914.000000       9914.000000  \\\n","mean         -73.974722        40.751041         -73.973657         40.751743   \n","std            0.042774         0.033541           0.039072          0.035435   \n","min          -74.252193        40.573143         -74.263242         40.568973   \n","25%          -73.992501        40.736125         -73.991247         40.735254   \n","50%          -73.982326        40.753051         -73.980015         40.754065   \n","75%          -73.968013        40.767113         -73.964059         40.768757   \n","max          -72.986532        41.709555         -72.990963         41.696683   \n","\n","       passenger_count  \n","count      9914.000000  \n","mean          1.671273  \n","std           1.278747  \n","min           1.000000  \n","25%           1.000000  \n","50%           1.000000  \n","75%           2.000000  \n","max           6.000000  "]},"metadata":{}}]},{"cell_type":"markdown","source":"# Adjusting the dataset according to test dataset\n\nWell, this is not always a good idea to do this but here i found a lot of noise in the train dataset thus, i'm implimenting the filter","metadata":{}},{"cell_type":"code","source":"# This is the helper function that i created to filter according to test dataset.\n\ndef remove_outliers(df):\n    return df[(df['fare_amount'] >= 1.) & \n              (df['fare_amount'] <= 500.) &\n              (df['pickup_longitude'] >= -75) & \n              (df['pickup_longitude'] <= -72) & \n              (df['dropoff_longitude'] >= -75) & \n              (df['dropoff_longitude'] <= -72) & \n              (df['pickup_latitude'] >= 40) & \n              (df['pickup_latitude'] <= 42) & \n              (df['dropoff_latitude'] >=40) & \n              (df['dropoff_latitude'] <= 42) & \n              (df['passenger_count'] >= 1) & \n              (df['passenger_count'] <= 6)]","metadata":{},"execution_count":5,"outputs":[]},{"cell_type":"code","source":"train=remove_outliers(train)","metadata":{},"execution_count":6,"outputs":[]},{"cell_type":"markdown","source":"This code snippet showcases a helper function called `remove_outliers()`. The purpose of this function is to filter out outliers from a given DataFrame, specifically based on certain criteria related to the New York City taxi fare dataset.\n\nThe function takes a DataFrame `df` as input and applies a series of conditions to filter out rows that are considered outliers. Let's break down the conditions:\n\n1. `df['fare_amount'] >= 1.0` and `df['fare_amount'] <= 500.0`: This condition filters out rows where the fare amount is less than 1 dollar or greater than 500 dollars. It helps remove extremely low or high fare values that may be considered outliers.\n\n2. Longitude and Latitude conditions:\n   - `df['pickup_longitude'] >= -75` and `df['pickup_longitude'] <= -72`: These conditions ensure that the pickup longitude values are within the geographical boundaries of New York City.\n   - `df['dropoff_longitude'] >= -75` and `df['dropoff_longitude'] <= -72`: Similar to the pickup longitude conditions, these ensure that the dropoff longitude values fall within the acceptable range.\n   - `df['pickup_latitude'] >= 40` and `df['pickup_latitude'] <= 42`: These conditions restrict the pickup latitude values to the geographical boundaries of New York City.\n   - `df['dropoff_latitude'] >= 40` and `df['dropoff_latitude'] <= 42`: Similarly, these conditions ensure that the dropoff latitude values are within the acceptable range.\n\n3. `df['passenger_count'] >= 1` and `df['passenger_count'] <= 6`: These conditions filter out rows where the passenger count is less than 1 or greater than 6. It eliminates unlikely or erroneous values for the number of passengers.\n\nBy applying these conditions, the `remove_outliers()` function returns a filtered DataFrame that contains rows satisfying all the specified criteria. This helps clean the dataset by removing outliers that may adversely affect the performance of the machine learning model.\n\nIt's important to note that the specific values used in these conditions are based on domain knowledge and assumptions about the New York City taxi fare dataset. Adjusting these values may be necessary depending on the context and characteristics of the data.","metadata":{}},{"cell_type":"markdown","source":"# Feature engineering\n\nThis is an important set in ML. I've tried nearly 10 features but found these only work in favour of the model. I'll attach the features i've tried at the end of the notebook for your refference.","metadata":{"execution":{"iopub.execute_input":"2023-05-16T07:22:24.676644Z","iopub.status.busy":"2023-05-16T07:22:24.675248Z","iopub.status.idle":"2023-05-16T07:22:24.683790Z","shell.execute_reply":"2023-05-16T07:22:24.682526Z","shell.execute_reply.started":"2023-05-16T07:22:24.676595Z"}}},{"cell_type":"markdown","source":"# 1. add ride distances","metadata":{}},{"cell_type":"code","source":"# This function return distance calcuated from 4 points\ndef haversine_np(lon1, lat1, lon2, lat2):\n    \n    \"\"\"Calculate the great circle distance between two points\n    on the earth (specified in decimal degrees)\n\n    All args must be of equal length.\"\"\"\n    \n    lon1, lat1, lon2, lat2 = map(np.radians, [lon1, lat1, lon2, lat2])\n\n    dlon = lon2 - lon1\n    dlat = lat2 - lat1\n\n    a = np.sin(dlat/2.0)**2 + np.cos(lat1) * np.cos(lat2) * np.sin(dlon/2.0)**2\n\n    c = 2 * np.arcsin(np.sqrt(a))\n    km = 6367 * c\n    return km","metadata":{"execution":{"iopub.execute_input":"2023-05-17T16:46:39.041521Z","iopub.status.busy":"2023-05-17T16:46:39.041149Z","iopub.status.idle":"2023-05-17T16:46:39.049334Z","shell.execute_reply":"2023-05-17T16:46:39.048151Z","shell.execute_reply.started":"2023-05-17T16:46:39.041486Z"}},"execution_count":8,"outputs":[]},{"cell_type":"code","source":"train['trip_distance'] = haversine_np(train['pickup_longitude'], train['pickup_latitude'], train['dropoff_longitude'], train['dropoff_latitude'])","metadata":{"execution":{"iopub.execute_input":"2023-05-17T16:46:39.051614Z","iopub.status.busy":"2023-05-17T16:46:39.051197Z","iopub.status.idle":"2023-05-17T16:46:39.277485Z","shell.execute_reply":"2023-05-17T16:46:39.276791Z","shell.execute_reply.started":"2023-05-17T16:46:39.051578Z"}},"execution_count":9,"outputs":[]},{"cell_type":"code","source":"# for test dataset\ntest['trip_distance'] = haversine_np(test['pickup_longitude'], test['pickup_latitude'], test['dropoff_longitude'], test['dropoff_latitude'])","metadata":{"execution":{"iopub.execute_input":"2023-05-17T16:46:39.279244Z","iopub.status.busy":"2023-05-17T16:46:39.278907Z","iopub.status.idle":"2023-05-17T16:46:39.289885Z","shell.execute_reply":"2023-05-17T16:46:39.288905Z","shell.execute_reply.started":"2023-05-17T16:46:39.279211Z"}},"execution_count":10,"outputs":[]},{"cell_type":"markdown","source":"This code snippet presents a helper function called `haversine_np()` that calculates the great circle distance between two points on the Earth, given their decimal degrees of longitude and latitude.\n\nThe function takes four input parameters: `lon1`, `lat1`, `lon2`, and `lat2`, representing the longitude and latitude coordinates of two points. The coordinates can be provided as individual values or as arrays of equal length.\n\nThe function returns the calculated distance in kilometers.\n\nBy using the haversine formula, this function provides an accurate estimation of distances between two points on the Earth's surface. It can be particularly useful in geographic analysis or applications that require distance calculations, such as route planning or location-based services.","metadata":{}},{"cell_type":"markdown","source":"# 2. add date parts","metadata":{}},{"cell_type":"code","source":"# helper function to extract useful info from any datetime column for ML\ndef extract_from_date(df):\n    import datetime\n    for col in df.columns:\n        if 'date' in col:\n            if isinstance(df[col].iloc[0], pd.Timestamp):\n                df['year'] = df[col].dt.year\n                df['month'] = df[col].dt.month\n                df['day'] = df[col].dt.day\n                df['day_of_week']=df[col].dt.dayofweek\n                df['week_of_year'] = df[col].dt.isocalendar().week\n                df['week_of_year'] = df['week_of_year'].astype(int)\n                df['hour'] = df[col].dt.hour\n            else:\n                df[col] = pd.to_datetime(df[col])-datetime.timedelta(hours=4)  #adjusting for the timezone\n                df[col] = df[col].dt.tz_localize(None)\n                df['year'] = df[col].dt.year\n                df['month'] = df[col].dt.month\n                df['day'] = df[col].dt.day\n                df['day_of_week']=df[col].dt.dayofweek\n                df['week_of_year'] = df[col].dt.isocalendar().week\n                df['week_of_year'] = df['week_of_year'].astype(int)\n                df['hour'] = df[col].dt.hour;","metadata":{},"execution_count":11,"outputs":[]},{"cell_type":"code","source":"extract_from_date(train)","metadata":{},"execution_count":12,"outputs":[]},{"cell_type":"code","source":"# for test dataset\nextract_from_date(test)","metadata":{},"execution_count":13,"outputs":[]},{"cell_type":"markdown","source":"In this code snippet, we have a helper function called `extract_from_date()`. The purpose of this function is to extract useful information from a datetime column in a given DataFrame for machine learning purposes.\n\nThe function takes a DataFrame `df` as input. It iterates over each column name in the DataFrame and checks if the column name contains the word 'date'. This check allows the function to identify columns that contain datetime information.\n\nIf the column contains datetime information and the first value of the column is of type `pd.Timestamp`, the function performs the following operations:\n\n- `df['year'] = df[col].dt.year`: This line extracts the year component from the datetime column and creates a new column named 'year' in the DataFrame.\n\n- `df['month'] = df[col].dt.month`: Similarly, this line extracts the month component and creates a new column named 'month' in the DataFrame.\n\n- `df['day'] = df[col].dt.day`: This line extracts the day component and creates a new column named 'day' in the DataFrame.\n\n- `df['day_of_week'] = df[col].dt.dayofweek`: This line extracts the day of the week (0 for Monday, 1 for Tuesday, and so on) and creates a new column named 'day_of_week' in the DataFrame.\n\n- `df['week_of_year'] = df[col].dt.isocalendar().week`: This line extracts the week of the year using the `isocalendar().week` method and creates a new column named 'week_of_year' in the DataFrame. The week numbers are represented as integers.\n\n- `df['hour'] = df[col].dt.hour`: This line extracts the hour component and creates a new column named 'hour' in the DataFrame.\n\nIf the column contains datetime information but the first value is not of type `pd.Timestamp`, it means the column needs to be converted to a datetime format. The function performs the following operations:\n\n- `df[col] = pd.to_datetime(df[col]) - datetime.timedelta(hours=4)`: This line converts the column to a datetime format using `pd.to_datetime()`. It then subtracts 4 hours from each value to adjust for the timezone difference. This line assumes the original datetime values were in a different timezone and converts them to a common timezone.\n\n- `df[col] = df[col].dt.tz_localize(None)`: After adjusting the timezone, this line removes the timezone information from the datetime values, leaving only the date and time components.\n\nThe function then proceeds to extract the same useful information as mentioned earlier (year, month, day, day_of_week, week_of_year, hour) and creates new columns in the DataFrame for each of these components.\n\nBy applying this function, we can extract important temporal features from datetime columns in a dataset. These features can be valuable inputs for machine learning models, enabling them to capture time-based patterns and dependencies in the data.","metadata":{}},{"cell_type":"markdown","source":"# 3. Popular Landmarks","metadata":{}},{"cell_type":"code","source":"jfk_lonlat = -73.7781, 40.6413\nlga_lonlat = -73.8740, 40.7769\newr_lonlat = -74.1745, 40.6895\nmet_lonlat = -73.9632, 40.7794\nwtc_lonlat = -74.0099, 40.7126\n\ndef add_landmark_dropoff_distance(df, landmark_name, landmark_lonlat):\n    lon, lat = landmark_lonlat\n    df[landmark_name + '_drop_distance'] = haversine_np(lon, lat, df['dropoff_longitude'], df['dropoff_latitude']);\n    \n    \nfor a_df in [train, test]:\n    for name, lonlat in [('jfk', jfk_lonlat), ('lga', lga_lonlat), ('ewr', ewr_lonlat), ('met', met_lonlat), ('wtc', wtc_lonlat)]:\n        add_landmark_dropoff_distance(a_df, name, lonlat)","metadata":{},"execution_count":14,"outputs":[]},{"cell_type":"markdown","source":"In this code snippet, we have a function called `add_landmark_dropoff_distance()` and a loop that applies this function to both the `train` and `test` datasets. The purpose of this code is to calculate the dropoff distance from specific landmarks and add this information as new columns in the datasets.\n\nThe function `add_landmark_dropoff_distance()` takes three parameters: `df`, which represents the DataFrame to which the new column will be added, `landmark_name`, which is a string specifying the name of the landmark, and `landmark_lonlat`, which is a tuple containing the longitude and latitude coordinates of the landmark.\n\nWithin the function, the longitude (`lon`) and latitude (`lat`) values are extracted from the `landmark_lonlat` tuple. Then, the `haversine_np()` function is called to calculate the distance between the landmark coordinates and the dropoff longitude and latitude values in the DataFrame `df`. The calculated distance is stored in a new column, named with the `landmark_name` and the suffix '_drop_distance'.\n\nIn the loop that follows, the `add_landmark_dropoff_distance()` function is applied to each dataset (`train` and `test`) for a list of predefined landmarks. For each landmark, a new column is added to the dataset, containing the dropoff distance from that landmark.\n\nThe predefined landmarks and their corresponding longitude and latitude coordinates are as follows:\n\n- JFK Airport: Longitude -73.7781, Latitude 40.6413\n- LGA Airport: Longitude -73.8740, Latitude 40.7769\n- EWR Airport: Longitude -74.1745, Latitude 40.6895\n- Metropolitan Museum of Art: Longitude -73.9632, Latitude 40.7794\n- World Trade Center: Longitude -74.0099, Latitude 40.7126\n\nBy executing this code, the `train` and `test` datasets will be expanded with additional columns representing the distance between each dropoff location and the predefined landmarks. This information can be useful for machine learning models to capture the proximity of dropoff locations to significant landmarks, enabling them to potentially learn location-specific patterns.","metadata":{}},{"cell_type":"markdown","source":"# Exploratory Data Analysis","metadata":{}},{"cell_type":"markdown","source":"### 1. Histogram of Fare Amount:\nThis plot provides an overview of the distribution of fare amounts.","metadata":{}},{"cell_type":"code","source":"plt.hist(train['fare_amount'], bins=20)\nplt.xlabel('Fare Amount')\nplt.ylabel('Frequency')\nplt.title('Distribution of Fare Amount')\nplt.show()","metadata":{},"execution_count":21,"outputs":[{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 1000x600 with 1 Axes>"]},"metadata":{"needs_background":"light"}}]},{"cell_type":"markdown","source":"### Scatter Plot of Pickup and Dropoff Locations:\nThis plot allows you to visualize the spatial distribution of the pickup and dropoff locations on a scatter plot.","metadata":{}},{"cell_type":"code","source":"sampled_data = train.sample(n=10000)  # Adjust the sample size as needed\nplt.scatter(sampled_data['pickup_longitude'], sampled_data['pickup_latitude'], label='Pickup')\nplt.scatter(sampled_data['dropoff_longitude'], sampled_data['dropoff_latitude'], label='Dropoff')\nplt.xlabel('Longitude')\nplt.ylabel('Latitude')\nplt.title('Pickup and Dropoff Locations')\nplt.legend()\nplt.show()","metadata":{},"execution_count":25,"outputs":[{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 1000x600 with 1 Axes>"]},"metadata":{"needs_background":"light"}}]},{"cell_type":"markdown","source":"### Bar Plot of Passenger Counts:\nThis plot provides insights into the distribution of passenger counts in the dataset.","metadata":{}},{"cell_type":"code","source":"passenger_counts = train['passenger_count'].value_counts()\nplt.bar(passenger_counts.index, passenger_counts.values)\nplt.xlabel('Passenger Count')\nplt.ylabel('Frequency')\nplt.title('Distribution of Passenger Counts')\nplt.show()","metadata":{},"execution_count":26,"outputs":[{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 1000x600 with 1 Axes>"]},"metadata":{"needs_background":"light"}}]},{"cell_type":"markdown","source":"### Line Plot of Fare Amount over Time:\nThis plot shows the trend of fare amounts over time, allowing you to identify any patterns or seasonality.","metadata":{}},{"cell_type":"code","source":"# df['pickup_datetime'] = pd.to_datetime(df['pickup_datetime'])\n# df['date'] = df['pickup_datetime'].dt.date\nfare_over_time = train.groupby('pickup_datetime')['fare_amount'].mean()\n\nplt.plot(fare_over_time.index, fare_over_time.values)\nplt.xlabel('Date')\nplt.ylabel('Average Fare Amount')\nplt.title('Fare Amount over Time')\nplt.xticks(rotation=45)\nplt.show()","metadata":{},"execution_count":27,"outputs":[{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 1000x600 with 1 Axes>"]},"metadata":{"needs_background":"light"}}]},{"cell_type":"markdown","source":"### Box Plot of Fare Amount by Day of Week:\nThis plot helps analyze the variation in fare amounts based on the day of the week.","metadata":{}},{"cell_type":"code","source":"# df['day_of_week'] = df['pickup_datetime'].dt.dayofweek\nsns.boxplot(x=train['day_of_week'], y=train['fare_amount'])\nplt.xlabel('Day of Week')\nplt.ylabel('Fare Amount')\nplt.title('Fare Amount by Day of Week')\nplt.show()","metadata":{},"execution_count":29,"outputs":[{"output_type":"display_data","data":{"image/png":"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\n","text/plain":["<Figure size 1000x600 with 1 Axes>"]},"metadata":{"needs_background":"light"}}]},{"cell_type":"markdown","source":"# Converting to parquets\n\nThis makes the size of the dataset smaller also helps in faster ML modelling","metadata":{}},{"cell_type":"code","source":"train.to_parquet('train.parquet')\ntest.to_parquet('test.parquet')","metadata":{},"execution_count":30,"outputs":[]},{"cell_type":"markdown","source":"# splitting into train and test","metadata":{}},{"cell_type":"code","source":"train.columns","metadata":{},"execution_count":31,"outputs":[{"execution_count":31,"output_type":"execute_result","data":{"text/plain":["Index(['key', 'fare_amount', 'pickup_datetime', 'pickup_longitude',\n","       'pickup_latitude', 'dropoff_longitude', 'dropoff_latitude',\n","       'passenger_count', 'trip_distance', 'year', 'month', 'day',\n","       'day_of_week', 'week_of_year', 'hour', 'jfk_drop_distance',\n","       'lga_drop_distance', 'ewr_drop_distance', 'met_drop_distance',\n","       'wtc_drop_distance'],\n","      dtype='object')"]},"metadata":{}}]},{"cell_type":"code","source":"input_cols = ['pickup_longitude',\n       'pickup_latitude', 'dropoff_longitude', 'dropoff_latitude',\n       'passenger_count', 'trip_distance', 'year', 'month', 'day',\n       'day_of_week', 'week_of_year', 'hour', 'jfk_drop_distance',\n       'lga_drop_distance', 'ewr_drop_distance', 'met_drop_distance',\n       'wtc_drop_distance']\ntarget_col = 'fare_amount'","metadata":{},"execution_count":32,"outputs":[]},{"cell_type":"code","source":"from sklearn.model_selection import train_test_split\ntrain_df, val_df = train_test_split(train, test_size=0.2, random_state=42)","metadata":{},"execution_count":33,"outputs":[]},{"cell_type":"code","source":"train_inputs = train_df[input_cols]\ntrain_targets = train_df[target_col]\nval_inputs = val_df[input_cols]\nval_targets = val_df[target_col]\ntest_inputs = test[input_cols]","metadata":{},"execution_count":34,"outputs":[]},{"cell_type":"code","source":"def rmse(model):\n    model.fit(train_inputs, train_targets)\n    train_preds=model.predict(train_inputs)\n    val_preds=model.predict(val_inputs)\n    train_rmse = mean_squared_error(train_targets, train_preds, squared=False)\n    val_rmse = mean_squared_error(val_targets, val_preds, squared=False)\n    return train_rmse,val_rmse","metadata":{},"execution_count":35,"outputs":[]},{"cell_type":"markdown","source":"The `rmse` function calculates the Root Mean Squared Error (RMSE) for a given model. Here's a brief explanation of the code:\n\n1. The `model.fit(train_inputs, train_targets)` line fits the model to the training data, where `train_inputs` are the input features and `train_targets` are the corresponding target values.\n\n2. The `train_preds = model.predict(train_inputs)` line uses the trained model to make predictions on the training data.\n\n3. The `val_preds = model.predict(val_inputs)` line uses the trained model to make predictions on the validation data.\n\n4. The `train_rmse = mean_squared_error(train_targets, train_preds, squared=False)` line calculates the RMSE between the actual target values (`train_targets`) and the predicted target values on the training data (`train_preds`).\n\n5. The `val_rmse = mean_squared_error(val_targets, val_preds, squared=False)` line calculates the RMSE between the actual target values on the validation data (`val_targets`) and the predicted target values on the validation data (`val_preds`).\n\n6. Finally, the function returns the calculated RMSE values for both the training and validation sets using `return train_rmse, val_rmse`.\n\nNote that the code assumes the presence of the necessary libraries and data (`train_inputs`, `train_targets`, `val_inputs`, `val_targets`) for training and validation.","metadata":{}},{"cell_type":"markdown","source":"# training models","metadata":{}},{"cell_type":"code","source":"from sklearn.linear_model import LinearRegression\nfrom sklearn.metrics import mean_squared_error\n\nlinear_regression = LinearRegression()\n\nrmse(linear_regression)","metadata":{},"execution_count":36,"outputs":[{"execution_count":36,"output_type":"execute_result","data":{"text/plain":["(5.104621726502164, 5.129603743574691)"]},"metadata":{}}]},{"cell_type":"code","source":"from sklearn.linear_model import Ridge\nridge_regression = Ridge(random_state=42)\n\n\nrmse(ridge_regression)","metadata":{},"execution_count":37,"outputs":[{"execution_count":37,"output_type":"execute_result","data":{"text/plain":["(5.104621780651683, 5.129606187145003)"]},"metadata":{}}]},{"cell_type":"code","source":"from xgboost import XGBRegressor\ngradient_boosting = XGBRegressor(random_state=42, n_jobs=-1, objective='reg:squarederror',tree_method = 'gpu_hist')\n\nrmse(gradient_boosting)","metadata":{},"execution_count":38,"outputs":[{"execution_count":38,"output_type":"execute_result","data":{"text/plain":["(3.5256763, 3.6629467)"]},"metadata":{}}]},{"cell_type":"markdown","source":"I tried again a bunch od models with _Hyperparameter Tuning_ and girdseachcv but this simple baseline model was better than the fined tunes ones also that uses a lot of computational power to achive very slight accuracy. Thus i'm stopping with this model itsef.\n\nAlways in ML a balance between computational power and accuracy is important rather than training days of ML model to get slight increase in accuracy","metadata":{}},{"cell_type":"markdown","source":"# Submitting model","metadata":{}},{"cell_type":"code","source":"from IPython.display import display, FileLink\n\ndef predict_and_submit(model, fname):\n    test_preds = model.predict(test_inputs)\n    sub_df = pd.read_csv('/kaggle/input/new-york-city-taxi-fare-prediction/sample_submission.csv')\n    sub_df['fare_amount'] = test_preds\n    sub_df.to_csv(fname, index=None)\n    return display(FileLink(fname))","metadata":{},"execution_count":41,"outputs":[]},{"cell_type":"code","source":"predict_and_submit(gradient_boosting,'submission.csv')","metadata":{},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# Things i tried","metadata":{}},{"cell_type":"markdown","source":"## setting train according to test","metadata":{}},{"cell_type":"code","source":"\"\"\"# This is the helper function that i created to filter according to test dataset.\n# This is not that complex and its self explanatory.\n\ndef filter_for_test(train, test):\n    train = train[(train['fare_amount'] >= 0)]  #need to check if the fares can be zero\n    for i in test.columns:\n        if i not in [\"fare_amount\", \"pickup_datetime\", \"key\"]:\n            test_col_range = (test[i].min(), test[i].max())\n            train = train[(train[i] >= test_col_range[0]) & (train[i] <= test_col_range[1])]\n    train.reset_index(drop=True, inplace=True)  # Reset index after filtering\n    return train\n    \n    \ntrain=filter_for_test(train,test)\"\"\"","metadata":{},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"## add near landmark check","metadata":{}},{"cell_type":"code","source":"\"\"\"import numpy as np\n\nlandmarks = {\n    \"Statue of Liberty\": (-74.0445, 40.6892),\n    \"Empire State Building\": (-73.9857, 40.7484),\n    \"Times Square\": (-73.9851, 40.7589),\n    \"Central Park\": (-73.9654, 40.7829),\n    \"Brooklyn Bridge\": (-73.9968, 40.7061),\n    \"One World Trade Center\": (-74.0134, 40.7130),\n    \"Grand Central Terminal\": (-73.9772, 40.7527),\n    \"Metropolitan Museum of Art\": (-73.9632, 40.7794),\n    \"Rockefeller Center\": (-73.9787, 40.7587),\n    \"High Line\": (-74.0048, 40.7479),\n    \"9/11 Memorial\": (-74.013327, 40.711524),\n    \"Union Square\": (-73.9903, 40.7359),\n    \"Chinatown\": (-73.9965, 40.7158),\n    \"Veselka\": (-73.9827, 40.729),\n    \"SoHo\": (-74.0024, 40.7247),\n    \"Greenwich Village\": (-74.0018, 40.7336),\n    \"Harlem\": (-73.9442, 40.8079),\n    \"Subway\": (-73.9871, 40.7409),\n    \"LaGuardia Airport\": (-73.874, 40.7769),\n    \"JFK Airport\": (-73.7781, 40.6413),\n    \"Newark Liberty International Airport\": (-74.1724, 40.6895),\n    \"Brooklyn Battery Tunnel\": (-74.0048, 40.7046),\n    \"Downtown Brooklyn\": (-73.984, 40.6932),\n    \"Manhattan Bridge\": (-73.9897, 40.7071),\n    \"Williamsburg Bridge\": (-73.9654, 40.7126),\n    \"Lower Manhattan\": (-74.0121, 40.7074),\n    \"14th Street\": (-74.0003, 40.7409),\n    \"23rd Street\": (-73.9911, 40.7441),\n    \"34th Street\": (-74.0018, 40.7527),\n    \"42nd St\": (-73.9878, 40.7559),\n    \"57th St\": (-73.9776, 40.7644),\n    \"Queens Midtown Tunnel\": (-73.9666, 40.7448),\n    \"Long Island City\": (-73.9396, 40.7536),\n    \"Queensboro Bridge\": (-73.953, 40.7572)\n}\n\ndef is_near_landmark(coord, landmarks, distance_threshold):\n    for landmark, landmark_coord in landmarks.items():\n        dist = haversine_np(coord[0], coord[1], landmark_coord[0], landmark_coord[1])\n        if dist <= distance_threshold:\n            return 1\n    return 0\n\ndistance_threshold = 2  # Distance threshold in kilometers\"\"\"","metadata":{},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"## add airport distances","metadata":{}},{"cell_type":"code","source":"\"\"\"def calculate_distances(df):\n    jfk_coord = (40.639722, -73.778889)\n    ewr_coord = (40.6925, -74.168611)\n    lga_coord = (40.77725, -73.872611)\n    sol_coord = (40.6892, -74.0445)  # Statue of Liberty\n    nyc_coord = (40.7141667, -74.0063889)\n    \n    df['jfk_distance'] = haversine_np(df['pickup_longitude'], df['pickup_latitude'], jfk_coord[1], jfk_coord[0]) + haversine_np(df['dropoff_longitude'], df['dropoff_latitude'], jfk_coord[1], jfk_coord[0])\n    df['ewr_distance'] = haversine_np(df['pickup_longitude'], df['pickup_latitude'], ewr_coord[1], ewr_coord[0]) + haversine_np(df['dropoff_longitude'], df['dropoff_latitude'], ewr_coord[1], ewr_coord[0])\n    df['lga_distance'] = haversine_np(df['pickup_longitude'], df['pickup_latitude'], lga_coord[1], lga_coord[0]) + haversine_np(df['dropoff_longitude'], df['dropoff_latitude'], lga_coord[1], lga_coord[0])\n    df['sol_distance'] = haversine_np(df['pickup_longitude'], df['pickup_latitude'], sol_coord[1], sol_coord[0]) + haversine_np(df['dropoff_longitude'], df['dropoff_latitude'], sol_coord[1], sol_coord[0])\n    df['nyc_distance'] = haversine_np(df['pickup_longitude'], df['pickup_latitude'], nyc_coord[1], nyc_coord[0]) + haversine_np(df['dropoff_longitude'], df['dropoff_latitude'], nyc_coord[1], nyc_coord[0])\"\"\"","metadata":{},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"## add distance clusters","metadata":{}},{"cell_type":"code","source":"\"\"\"from sklearn.cluster import KMeans\n\n# Select the 'trip_distance' and 'fare_amount' columns\ntrain_data = train[['trip_distance']]\n\n\n# Perform K-means clustering\ncluster_range = range(1, 10)  # Range of cluster sizes to try\ninertia_values = []  # To store the sum of squared distances within clusters (inertia) for each cluster size\n\nfor k in cluster_range:\n    kmeans = KMeans(n_clusters=k, random_state=42,n_init='auto')\n    kmeans.fit(train_data)\n    inertia_values.append(kmeans.inertia_) \n\n# Plot the elbow curve\nplt.plot(cluster_range, inertia_values, marker='o')\nplt.xlabel('Number of clusters')\nplt.ylabel('Sum of squared distances')\nplt.title('Elbow Method')\nplt.show()\n\n# Perform K-means clustering with the optimal cluster size\noptimal_cluster_size = 5  # Replace with the cluster size determined from the elbow method\nkmeans = KMeans(n_clusters=optimal_cluster_size, random_state=42,n_init='auto')\nkmeans.fit(train_data)\nlabels = kmeans.labels_\ntrain['distance_clusters']=labels\n\n# Plot the clusters on a scatter plot\nplt.scatter(train_data['trip_distance'], np.zeros_like(train_data['trip_distance']), c=labels, cmap='viridis')\nplt.scatter(kmeans.cluster_centers_[:, 0], np.zeros_like(kmeans.cluster_centers_[:, 0]), marker='x', color='red', label='Cluster Centers')\nplt.xlabel('Trip Distance')\nplt.title('K-means Clustering')\nplt.legend()\nplt.show()\"\"\"","metadata":{},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"## add bearing direction","metadata":{}},{"cell_type":"code","source":"\"\"\"import math\n\ndef calculate_bearing(lat1, lon1, lat2, lon2):\n    \"\"\"\n    Calculates the initial bearing between two sets of coordinates.\n\n    Arguments:\n    lat1 -- latitude of the first coordinate (in decimal degrees)\n    lon1 -- longitude of the first coordinate (in decimal degrees)\n    lat2 -- latitude of the second coordinate (in decimal degrees)\n    lon2 -- longitude of the second coordinate (in decimal degrees)\n\n    Returns:\n    bearing -- initial bearing in degrees\n    \"\"\"\n\n    # Convert decimal degrees to radians\n    lat1 = math.radians(lat1)\n    lon1 = math.radians(lon1)\n    lat2 = math.radians(lat2)\n    lon2 = math.radians(lon2)\n\n    # Calculate differences in longitudes and latitudes\n    dLon = lon2 - lon1\n\n    # Calculate bearing\n    y = math.sin(dLon) * math.cos(lat2)\n    x = math.cos(lat1) * math.sin(lat2) - math.sin(lat1) * math.cos(lat2) * math.cos(dLon)\n    bearing = math.degrees(math.atan2(y, x))\n\n    # Convert bearing to a 0-360 degree range\n    #bearing = (bearing + 360) % 360\n\n    return bearing\n\n\n# Define a function to combine bearing and distance\ndef combine_bearing_distance(distance, bearing):\n    # Define a factor for combining bearing and distance\n    bearing_factor = 0.5  # Adjust this factor based on the importance you want to give to the bearing\n\n    # Multiply the distance by the bearing factor\n    combined_value = distance * bearing_factor\n\n    return combined_value\n    \n\ntrain['bearing'] = train.apply(lambda row: calculate_bearing(row['pickup_latitude'], row['pickup_longitude'],\n                                                      row['dropoff_latitude'], row['dropoff_longitude']), axis=1)\n\n# Create a new feature by combining bearing and distance\ntrain['bearing'] = train.apply(lambda row: combine_bearing_distance(row['trip_distance'], row['bearing']), axis=1)\"\"\"","metadata":{},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"## Coordinate Clusters","metadata":{}},{"cell_type":"code","source":"\"\"\"from sklearn.cluster import KMeans\ncoordinates = train[['pickup_longitude', 'pickup_latitude', 'dropoff_longitude', 'dropoff_latitude']]\n\n# K-MEANS:\n\n# Determine the optimal number of clusters using the elbow method\ninertia = []\nk_values = range(1, 30)  # Try different values of k\nfor k in k_values:\n    kmeans = KMeans(n_clusters=k, random_state=42,n_init='auto')\n    kmeans.fit(coordinates)\n    inertia.append(kmeans.inertia_)\n\n# Plot the elbow curve\nplt.plot(k_values, inertia, marker='o')\nplt.xlabel('Number of Clusters (k)')\nplt.ylabel('Inertia')\nplt.title('Elbow Method')\nplt.show()\n\n# Create a K-means clustering model\nkmeans = KMeans(n_clusters=2, random_state=42,n_init='auto')  #need to change no. of clustersaccoding to the train dataset\n\n# Fit the model to the coordinates\nkmeans.fit(coordinates)\n\n# Get the cluster labels\ncluster_labels = kmeans.labels_\n\ntrain['cluster_label'] = cluster_labels\"\"\"","metadata":{},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"##  Add Neighbourhood","metadata":{}},{"cell_type":"code","source":"\"\"\"neighborhoods = {\n    'Bronx': {\n        'latitude': (40.7855, 40.9176),\n        'longitude': (-73.9338, -73.7654)\n    },\n    'Manhattan': {\n        'latitude': (40.6987, 40.8780),\n        'longitude': (-74.0197, -73.9036)\n    },\n    'Queens': {\n        'latitude': (40.4957, 40.8007),\n        'longitude': (-73.9635, -73.7004)\n    },\n    'Brooklyn': {\n        'latitude': (40.5510, 40.7394),\n        'longitude': (-74.0563, -73.8331)\n    },\n    'Staten Island': {\n        'latitude': (40.4774, 40.6514),\n        'longitude': (-74.2591, -74.0536)\n    }\n}\n\n\n# Function to determine the neighborhood based on coordinates\ndef get_neighborhood(latitude, longitude):\n    for neighborhood, coords in neighborhoods.items():\n        lat_min, lat_max = coords['latitude']\n        lon_min, lon_max = coords['longitude']\n        if lat_min <= latitude <= lat_max and lon_min <= longitude <= lon_max:\n            return neighborhood\n    return 'Unknown'\n\n\ntrain['pickup_neighbourhood'] = train.apply(lambda row: get_neighborhood(row['pickup_latitude'], row['pickup_longitude']), axis=1)\ntrain['dropoff_neighbourhood'] = train.apply(lambda row: get_neighborhood(row['dropoff_latitude'], row['dropoff_longitude']), axis=1)\"\"\"","metadata":{},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"## add Direction feature","metadata":{}},{"cell_type":"code","source":"\"\"\"def calculate_direction(d_lon, d_lat):\n    result = np.zeros(len(d_lon))\n    l = np.sqrt(d_lon**2 + d_lat**2)\n    result[d_lon>0] = (180/np.pi)*np.arcsin(d_lat[d_lon>0]/l[d_lon>0])\n    idx = (d_lon<0) & (d_lat>0)\n    result[idx] = 180 - (180/np.pi)*np.arcsin(d_lat[idx]/l[idx])\n    idx = (d_lon<0) & (d_lat<0)\n    result[idx] = -180 - (180/np.pi)*np.arcsin(d_lat[idx]/l[idx])\n    return result\n\ntrain[\"delta_lon\"] = train[\"pickup_longitude\"] - train[\"dropoff_longitude\"]\ntrain[\"delta_lat\"] = train[\"pickup_latitude\"] - train[\"dropoff_latitude\"]\ntrain['direction'] = calculate_direction(train.delta_lon, train.delta_lat)\"\"\"","metadata":{},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"## Fair type\n\nsources:\n\n[boolmberg](https://www.bloomberg.com/news/articles/2022-11-15/nyc-taxi-cab-fares-to-rise-23-in-first-increase-since-2012)\n\n[NYC GOV](https://www.nyc.gov/site/tlc/passengers/taxi-fare.page)","metadata":{}},{"cell_type":"code","source":"\"\"\"def fare_type(df):\n    df['fare_type'] = np.where(df['year'] < 2012, 0, 1);\n    \nfare_type(train)\"\"\"","metadata":{},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"## overnight and rush hour surcharges.\n\nsources:\n\n[boolmberg](https://www.bloomberg.com/news/articles/2022-11-15/nyc-taxi-cab-fares-to-rise-23-in-first-increase-since-2012)\n\n[NYC GOV](https://www.nyc.gov/site/tlc/passengers/taxi-fare.page)","metadata":{}},{"cell_type":"code","source":"\"\"\"overnight=[20,21,22,23,0,1,2,3,4,5,6]\nrush_hour=[16,17,18,19]\n\ndef surcharge(df):\n    df['overnight'] = df['hour'].apply(lambda x: 1 if x in overnight else 0)\n    df['rush_hour'] = df['hour'].apply(lambda x: 1 if x in rush_hour else 0);\n    \n    \nsurcharge(train)\"\"\"","metadata":{},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"## improvement surcharge\n\nsources:\n\n[boolmberg](https://www.bloomberg.com/news/articles/2022-11-15/nyc-taxi-cab-fares-to-rise-23-in-first-increase-since-2012)\n\n[NYC GOV](https://www.nyc.gov/site/tlc/passengers/taxi-fare.page)","metadata":{}},{"cell_type":"code","source":"\"\"\"def improvement(df):\n    df['improvement_surcharge'] = np.where(df['year'] >= 2015, 1, 0);\n    \nimprovement(train)\"\"\"","metadata":{},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"### Airport Charges","metadata":{}},{"cell_type":"code","source":"\"\"\"def airport_charges(df):\n    airport_coordinates = {\n        'LaGuardia Airport': (-73.874, 40.7769),\n        'JFK Airport': (-73.7781, 40.6413),\n        'Newark Liberty International Airport': (-74.1724, 40.6895)\n    }\n    \n    def within_1km(row):\n        for airport, coordinates in airport_coordinates.items():\n            pickup_distance = haversine_np(row['pickup_longitude'], row['pickup_latitude'], coordinates[0], coordinates[1])\n            dropoff_distance = haversine_np(row['dropoff_longitude'], row['dropoff_latitude'], coordinates[0], coordinates[1])\n            if pickup_distance <= 1 or dropoff_distance <= 1:\n                return 1\n        return 0\n    \n    df['airport_charges'] = df.apply(within_1km, axis=1);\n    \n    \nairport_charges(train)\"\"\"","metadata":{},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"Even though some of the things i took it from the official NYC.gov website that didn't seemed to work for this dataset. If u think any of this might help the model feel free to use my code","metadata":{}},{"cell_type":"markdown","source":"# The End","metadata":{}}]}