Find Taxi fare

Taxi rate find

At the end of the journey, the amount of the toll is added to the measured price. Let us now use the library function of numpy 's lstsq to find the optimal weight column w . They may need to walk a few blocks to find a taxi stand nearby, but they are easy to spot.

What tech do you use to combat this outrageous cab fare?

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Cab fare

You can estimate a fare between any two adresses ( if the basic map of these adresses is known by the basic map engines, which is the case in most towns in the advanced world). When you find something useful or if you like the site, take a little bit of your own moment to help someone else by bringing in your own expense expertise.

Name the taxi tariff in your town!

New York City Taxi Fare Starter Kernel - Simple Linear Model

"cell": "metadata": "_uuuid": "b4578d48b219735043a4d2102119fb307d2fc83f", "cell_type": "transcript", "source" : "This is a fundamental starter core for the New York City Taxi Fare Prediction Playground Competition \nHere we use a single lineal modeling tool that uses the driving sector from the taxi's pick-up point to the parking point to predict the 'fare_amount' of each trip.

" Meta data": "_uuuid": "8f2839f25d086af736a60e9eeb907d3b93b93b6e0e5", "_cell_guid": "b1076dfc-b9ad-4769-8c92-a6c4dae69d19", "trustworthy":, "cell_type": "code", "source": "# Initial Python Environmental setup. "Meta data": "_cell_guid": "79c7e3d0-c299-4dcb-8224-4455121ee9b0", "_uuuid": "d629ff2d2480ee46fbb7e2d37f6b5fab8052498a", "trustworthy":, "cell_type": "code", "source": "train_df = Uplink ", "train_df = Uplink", "train_df = pd.

"Meta data": "trustworthy":, "_uuuid": "59f0595db44dd60044cfd0404824651a7c2bee87", "compressed":, "cell_type" : "code", "source" :

MetaDaten" : "_uuuuid" : "b1dbc7610bd467f1dfaf9042b5ec638eb2014aaf", "cell_type" : "markdown", "source" : "###### Explore et taille les valeurs aberrantes\n Voyons d'abord si l'on trouve des `NaN`s dans les données. Meta data": "trustworthy":, "_uuuid": "e808c7e75338b45ca30f9f261dfbc90845700624", "cell_type": "code", "source" : "print(train_df.isnull(). sum())". Meta data": "_uuuid": "29bc86f2fa8baa37f0c4eb4300f77a8cb69f12aa", "cell_type": "markdown", "source": "There is a small amount, so we delete it from the data set.

" Meta data": "trustworthy":, "_uuuid": "9d8f28e24f3d4ca55ad93692329680774c341376", "cell_type": "code", "source": "print('Old size: %d' % len(train_df))\ntrain_df= train_df. Meta data": "_uuuid": "6a045ef14c636ec726a5e8c349ca7e5fbb3a87c1", "cell_type": "markdown", "source": "Now we want to quickly chart a subsets of our trip sector feature to see their distributions.

" Meta adaten" : "vertrauenswürdig" :, "_uuuuuuid" : "97d0aaa1deab1c6cf0c97a4a3a12ba7007aada6c5", "cell_type" : "code", "source" : "plot = train_df Meta adaten" : "vertrauenswürdig" :, "_uuuuuuid" : "97d0aaa1deab1c6cf0c97a4a3a12ba7007aada6c5", "cell_type" : "code", "source" : "plot = train_df.iloc[:2000]. scatter('abs_diff_longitude','abs_diff_latitude')", "execution_count" : Meta data": "_uuuid": "22277d77f75e3177a5acaec9b820e0de6e869663", "cell_type": "markdown", "source": "We assume that most of these data are very small (probably between 0 and 1), because they should be different between GPS co-ordinates within a town.

)" Meta data": "trustworthy":, "_uuuid": "9703895e6c7e67b32c504f843b5ef19be2023964", "cell_type": "code", "source": "print('Old size: %d' % len(train_df))\ntrain_df= train_df[(train_df. "Meta data": "_uuuid": "2151480a168d291bc2f4fd014fdac4ab7b5f6560", "cell_type": "markdown", "source": "### Train our modell\nOur our modell will take the shape $X \\cdot t = y$, where $X$ is a array of entry functions and $y$ is a destination variables "fare_amount " for each line.

" Meta data": "trusted":, "_uuuid": "fb752441a1c1ce3e01d78452389ec48c95d52dc6", "cell_type": "code", "source": "# Construct and deliver an entry array of our straight-line model\n# using the traverse vector, plus a 1. 0 for a continuous binary term. 3.

" Meta data": "trustworthy":, "_uuuid": "85abbb09a27d2e1e2a15b261264b3c7cbdde39e4", "cell_type": "code", "source": "# The list search functions return several things, and we only take into account the real w-value. Meta data": "_uuuid": "4c11c9993467cd31c6be525f864eae24b0da364d", "cell_type": "markdown", "source" : "Those weightings undergo a rapid review of reason, as we would anticipate that the first two readings - the weightings for total length and width difference - would be favorable, as more range would mean a higher fare, and we would anticipate that the bike term would easily reflect the costs of a very brief trip.

Method:\n$w = (X^T \cdot X)^{-1} \cdot X^T \\cdot y$", "metadata" : "trustworthy" :, "_uuuid" : "4a629cdacdddd48a7ba9e8492b0e748cde819829", "cell_type" : "code", "source" : "w_OLS = np. Meta data": "_uuuid": "a70ed21b43d720282bbae70e934b1188be2bc382", "cell_type": "markdown", "source": "### forecasts about the test set now we download our test input and forecast the `fare_amounts for them with our learnt weight!

" Meta data": "trustworthy":, "_uuuid": "3cbf4836cf8c71dfb67d13a9621b18a8d487197e", "cell_type": "code", "source": "test_df = pd.read_csv('../input/test.csv')\ntest_df. dtypes", "execution_count" : Meta data": "trustworthy":, "_uuuid": "ddba4a856ff617411a641dfdf7635e47f969dff8", "cell_type": "code", "source": "# Reuse the above help functionality to include our feature set and create the entry grid.

Meta Daten" : "_uuuuid" : "80ed89470e25d75c0b99008b9c88861be9739da3", "cell_type" : "markdown", "source" : "## Ideas for Improvement\nLe résultat sera un $5 Meta Daten" : "_uuuuid" : "80ed89470e25d75c0b99008b9c88861be9739da3", "cell_type" : "markdown", "source" : "## Ideas for Improvement\nLe résultat sera un $5. 74 but you can do better than that ! We' re just looking at the differences between the starting point and the end point, but perhaps the real numbers - showing where the taxi is going in New York - would be useful.

Try finding more runaways to crop them, or design useful features crucifixes. "Meta Daten" : "_uuuuuid" : "8fd559ff5ca72a73091d5dfd5b7032522832e999", "cell_type" : "markdown", "source" : "Special thanks to Dan Becker, Will Cukierski, and Julia Elliot for review this Kernel and provisions ! "Meta data" : "kernelspec" : "display_name" : "Python 3", "language" : "python", "name" : "python3", "language_info":

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