jinglescode

jinglescode

,listing_id,name,neighbourhood_group_cleansed,latitude,longitude,property_type,room_type,accommodates,bathrooms,bedrooms,beds,amenities,price,guests_included,minimum_nights,number_of_reviews,review_scores_rating,review_scores_accuracy,review_scores_cleanliness,review_scores_checkin,review_scores_communication,review_scores_location,review_scores_value,estimated_revenue
0,241032,Stylish Queen Anne Apartment,Queen Anne,47.63628904,-122.3710252,Apartment,Entire home/apt,4,1,1,1,"{TV,""Cable TV"",Internet,""Wireless Internet"",""Air Conditioning"",Kitchen,Heating,""Family/Kid Friendly"",Washer,Dryer}",85,2,1,207,95,10,10,10,10,9,10,17595
1,953595,Bright & Airy Queen Anne Apartment,Queen Anne,47.63912312,-122.3656665,Apartment,Entire home/apt,4,1,1,1,"{TV,Internet,""Wireless Internet"",Kitchen,""Free Parking on Premises"",""Buzzer/Wireless Intercom"",Heating,""Family/Kid Friendly"",Washer,Dryer,""Smoke Detector"",""Carbon Monoxide Detector"",""First Aid Kit"",""Safety Card"",""Fire Extinguisher"",Essentials}",150,

jinglescode

jinglescode

"",id,latitude,longitude,accommodates,bathrooms,bedrooms,beds,guests_included,minimum_nights,number_of_reviews,review_scores_rating,review_scores_accuracy,review_scores_cleanliness,review_scores_checkin,review_scores_communication,review_scores_location,review_scores_value
count,3.818000e+03,3818.000000,3818.000000,3818.000000,3802.000000,3812.000000,3817.000000,3818.000000,3818.000000,3818.000000,3171.000000,3160.000000,3165.000000,3160.000000,3167.000000,3163.000000,3162.000000
mean,5.550111e+06,47.628961,-122.333103,3.349398,1.259469,1.307712,1.735394,1.672603,2.369303,22.223415,94.539262,9.636392,9.556398,9.786709,9.809599,9.608916,9.452245
std,2.962660e+06,0.043052,0.031745,1.977599,0.590369,0.883395,1.139480,1.311040,16.305902,37.730892,6.606083,0.698031,0.797274,0.595499,0.568211,0.629053,0.750259
min,3.335000e+03,47.505088,-122.417219,1.000000,0.000000,0.000000,1.000000,0.000000,1.000000,0.000000,20.000000,2.000000,3.000000,2.000000,2.000000,4.000000,2.000000
25%,3.258256e+06,47.609418,-122.354320,2.0

jinglescode

"Row #", "Label (Y)", "Features (X)"
1, 107.9674, "[127,135.25,138.25,149.19,158.13,157.5,155.13,84.75,82.75,82.37,81.81,87.81,93,89,92.12,92.12,89.62,85.75,89.44,85.56,84.81,86.25,85.75,94.69,104.44,107.25,113.19,117.94,113.81,109.94,105.87,104.25,110.62,105.25,96.62,104.25,105.37,113.06,104.12,96.87,105.06,106.37,105.87,109.31,110,113.62,128.06,127.37,134,137.81]"
2, 108.2624, "[135.25,138.25,149.19,158.13,157.5,155.13,84.75,82.75,82.37,81.81,87.81,93,89,92.12,92.12,89.62,85.75,89.44,85.56,84.81,86.25,85.75,94.69,104.44,107.25,113.19,117.94,113.81,109.94,105.87,104.25,110.62,105.25,96.62,104.25,105.37,113.06,104.12,96.87,105.06,106.37,105.87,109.31,110,113.62,128.06,127.37,134,137.81,141.75]"
3, 108.3312, "[138.25,149.19,158.13,157.5,155.13,84.75,82.75,82.37,81.81,87.81,93,89,92.12,92.12,89.62,85.75,89.44,85.56,84.81,86.25,85.75,94.69,104.44,107.25,113.19,117.94,113.81,109.94,105.87,104.25,110.62,105.25,96.62,104.25,105.37,113.06,104.12,96.87,105.06,106.37,105.87,109.31,110,113.62,128.06,127.37,134,137.81,1

jinglescode

function ComputeSMA(data, window_size)
{
  let r_avgs = [], avg_prev = 0;
  for (let i = 0; i <= data.length - window_size; i++){
    let curr_avg = 0.00, t = i + window_size;
    for (let k = i; k < t && k <= data.length; k++){
      curr_avg += data[k]['price'] / window_size;
    }
    r_avgs.push({ set: data.slice(i, i + window_size), avg: curr_avg });
    avg_prev = curr_avg;

jinglescode

async function trainModel(inputs, outputs, trainingsize, window_size, n_epochs, learning_rate, n_layers, callback){

  const input_layer_shape  = window_size;
  const input_layer_neurons = 100;

  const rnn_input_layer_features = 10;
  const rnn_input_layer_timesteps = input_layer_neurons / rnn_input_layer_features;

  const rnn_input_shape  = [rnn_input_layer_features, rnn_input_layer_timesteps];
  const rnn_output_neurons = 20;

jinglescode