Linear regression solution RMSE 34k

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s430705 2021-05-06 12:41:21 +02:00
parent 5c4bb10ddf
commit 6402b55d3b
3 changed files with 2047 additions and 0 deletions

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import pandas as pd
from pandas import DataFrame
from sklearn import preprocessing
from sklearn.linear_model import LinearRegression
import plotly.express as px
col_names = ["price", "mileage", "year", "brand", "engine_type", "engine_cap"]
col_names_in = ["mileage", "year", "brand", "engine_type", "engine_cap"]
df_train = pd.read_csv("train/train.tsv", error_bad_lines=False, header=None, sep="\t", names=col_names)
df = df_train.drop(df_train[df_train["price"] < 1000].index)
dev0 = pd.read_csv("dev-0/in.tsv", error_bad_lines=False, header=None, sep="\t", names=col_names_in)
testA = pd.read_csv("test-A/in.tsv", error_bad_lines=False, header=None, sep="\t", names=col_names_in)
def preprocess_data(df: DataFrame) -> DataFrame:
"""Prepare dataset to linear regression"""
df["brand"] = df["brand"].str.lower()
scaler = preprocessing.StandardScaler()
df[["mileage", "year", "engine_cap"]] = scaler.fit_transform(df[["mileage", "year", "engine_cap"]])
enc = preprocessing.LabelEncoder()
enc.fit(df[["brand"]])
df[["brand"]] = enc.transform(df[["brand"]])
enc.fit(df["engine_type"])
df[["engine_type"]] = enc.transform(df[["engine_type"]])
return df
df_train = preprocess_data(df_train)
dev0 = preprocess_data(dev0)
testA = preprocess_data(testA)
fig = px.imshow(df_train.corr())
fig.show()
Y_train = df_train["price"]
lm_model = LinearRegression()
lm_model.fit(df_train[["mileage", "year", "brand", "engine_cap"]], Y_train)
dev0_predicted = lm_model.predict(dev0[["mileage", "year", "brand", "engine_cap"]])
testA_predicted = lm_model.predict(testA[["mileage", "year", "brand", "engine_cap"]])
pd.Series(dev0_predicted).to_csv("dev-0/out.tsv", sep="\t", index=False, header=False)
pd.Series(testA_predicted).to_csv("test-A/out.tsv", sep="\t", index=False, header=False)

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