Home Courses Yellow taxi Demand prediction Newyork city Random Forest regression.

Random Forest regression.

Instructor: Applied AI Course Duration: 4 mins

This content is restricted. Please Login

Prev

Next

Linear regression.

Xgboost Regression

Taxi demand prediction in New York City

1.1

Business/Real world problem Overview

9 min

1.2

Objectives and Constraints

11 min

1.3

Mapping to ML problem :Data

8 min

1.4

Mapping to ML problem :dask dataframes

11 min

1.5

Mapping to ML problem :Fields/Features.

6 min

1.6

Mapping to ML problem :Time series forecasting/Regression

8 min

1.7

Mapping to ML problem :Performance metrics

6 min

1.8

Data Cleaning :Latitude and Longitude data

4 min

1.9

Data Cleaning :Trip Duration.

7 min

1.10

Data Cleaning :Speed.

5 min

1.11

Data Cleaning :Distance.

2 min

1.12

Data Cleaning :Fare

6 min

1.13

Data Cleaning :Remove all outliers/erroneous points

3 min

1.14

Data Preparation:Clustering/Segmentation

19 min

1.15

Data Preparation:Time binning

5 min

1.16

Data Preparation:Smoothing time-series data.

5 min

1.17

Data Preparation:Smoothing time-series data part2

2 min

1.18

Data Preparation: Time series and Fourier transforms.

13 min

Base line models

2.1

Ratios and previous-time-bin values

9 min

2.2

Simple moving average

8 min

2.3

Weighted Moving average.

5 min

2.4

Exponential weighted moving average

6 min

2.5

4 min

Regression models:

3.1

Train-Test split & Features

3 min

3.2

Linear regression.

3 min

3.3

Random Forest regression.

4 min

3.4

Xgboost Regression

2 min

3.5

Model comparison.

6 min

3.6

6 min