# Write a function names analyse_and_fit_lrm() which takes one arguments (a path to a dataset)

Learning Goal: I’m working on a machine learning exercise and need an explanation and answer to help me learn.Analyze the data and apply a linear regression modelGiven datatset which contains information abput house prices bin the California. Task is first to analyze the data abd then to apply a regression model to it.Dataset consists of following variablesPrice: Price of houseBedroom: Number of bedroomsSpace: space of houseRoom :Number of roomsLot: Width of lotTax: Amount of annual taxBathtoom: Number of bathroomsGarage: Number of parkingCondition: Condition of house(1if good , 0 otherwise)The values in some of the columns may be missing, So It must handle this property(E.g. by filtering out NA values from given column before calculating any statistics or dataframes that is dependent on itI expect to describe the relationship between Price (Which will be a dependent variable in the model) and all other variables (predictors) using a linear regression modelTo Fit a model to the data, We can either use built in functions or calculate the parametes of the model from scratch. If we choose the latter approach, here you will find all the equations you need to implement a least-squares method for calculating model parameters.Task details: Write a function names analyse_and_fit_lrm() which takes one arguments (a path to a dataset) and returns a names list of the following objects. ( the order and names of the objects should be same as below):Summary_list- a named list of length 3 with the following elements : Statistics: a numeric vector of length 5 specifying mean, standard deviation, median, minimum and maximum for avariable TAX for all the houses with two bathrooms and four bedrooms (you do not need to name elements of the vector).

Data_frame- a data frame with the observations for which Space is bigger than 800 ordered decreasing Price.

Number_of_observations – a numeric value corresponding to the number of obswervations for which the vaklue of a variable “Lot” is equal to or bigger than the 4th 5 quantile of this variable.

Regression_list- a named list of length 2 with the following elements Model_parameter- a numeric vector of length 9 giving the model parameters. The first element of the vector should be named Intercept, and all other elements should have the same name as the respective variable.

Price_prediction- a numeric value which corresponds to the prediction of the price (using the applied model) for a house with the following specific parameters: three bedrooms; 1500 Square feet of space; eight rooms;width of lot is 40; $1000 tax; two bathrooms; one space in the garage; house is in bad condition.

Apart from base R, you can use any package from the tidyverse collectionHints Do not call analyse_and_fit_lrm() function explicitly in your file. It will be automatically invoked with correct file_path argument during the execution of unit test.Data sample is like Tab separated table; Total 9 columnsPrice Bedroom Space Room Lot Tax Bathroom Garage Condition53 2 967 5 39 652 1.5 0 055 2 815 5 33 1000 1 2 156 3 900 5 35 897 1.5 1 058 3 1007 6 24 964 1.5 2 064 3 1100 7 50 1099 1.5 1.5 044 4 897 7 25 960 2 1 049 5 1400 8 NA 678 1 1 170 3 2261 6 29 2700 1 2 072 4 1290 8 NA 800 1.5 1.5 082 4 2104 9 40 1038 2.5 1 185 8 2240 12 50 1200 3 2 045 2 641 5 25 860 1 0 047 3 862 6 25 600 1 0 049 4 1043 7 30 676 1.5 0 056 4 1325 8 50 1287 1.5 0 060 2 782 5 25 834 1 0 062 3 1126 7 30 734 2 0 1