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6
.ipynb_checkpoints/Untitled-checkpoint.ipynb
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@ -1,6 +0,0 @@
{
"cells": [],
"metadata": {},
"nbformat": 4,
"nbformat_minor": 4
}

251
.ipynb_checkpoints/solution-checkpoint.ipynb
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{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd\n",
"import numpy as np\n",
"from pathlib import Path\n",
"import seaborn as sns\n",
"import matplotlib.pyplot as plt\n",
"import numpy as np\n",
"from sklearn.linear_model import LinearRegression"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## TRENING"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"NAMES = [\"Price\",\"Mileage\",\"Year\",\"Brand\",\"EngineType\",\"EngineCapacity\"]\n",
"TRAIN_BASE = pd.read_csv(\"train/train.tsv\", sep ='\\t', names=NAMES)"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"Y_TRAIN = np.array(TRAIN_BASE[\"Price\"])\n",
"X_TRAIN = np.array(TRAIN_BASE[[\"Mileage\",\"Year\",\"EngineCapacity\"]])"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"MODEL = LinearRegression().fit(X_TRAIN,Y_TRAIN)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## DEV-0"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
"NAMES = [\"Mileage\",\"Year\",\"Brand\",\"EngineType\",\"EngineCapacity\"]\n",
"FILE_BASE = pd.read_csv(\"dev-0/in.tsv\", sep ='\\t', names=NAMES)"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [],
"source": [
"X_TEST = np.array(FILE_BASE[[\"Mileage\",\"Year\",\"EngineCapacity\"]])"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [],
"source": [
"Y_TEST = MODEL.predict(X_TEST)"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [],
"source": [
"VALUES = np.array2string(Y_TEST, precision=5, separator='\\n',suppress_small=True)"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [],
"source": [
"VALUES = VALUES.split(\".\\n\")"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [],
"source": [
"OUTFILE = open(\"dev-0/out.tsv\", \"w\")"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"scrolled": false
},
"outputs": [],
"source": [
"for x in VALUES:\n",
" RESULT = x.replace(\" \",\"\")\n",
" RESULT = RESULT.replace(\"[\",\"\")\n",
" RESULT = RESULT.replace(\"]\",\"\")\n",
" OUTFILE.write(str(RESULT)+\"\\n\")"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [],
"source": [
"OUTFILE.close()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## TEST A"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [],
"source": [
"NAMES = [\"Mileage\",\"Year\",\"Brand\",\"EngineType\",\"EngineCapacity\"]\n",
"FILE_BASE = pd.read_csv(\"test-A/in.tsv\", sep ='\\t', names=NAMES)"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [],
"source": [
"X_TEST = np.array(FILE_BASE[[\"Mileage\",\"Year\",\"EngineCapacity\"]])"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [],
"source": [
"Y_TEST = MODEL.predict(X_TEST)"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [],
"source": [
"VALUES = np.array2string(Y_TEST, precision=5, separator='\\n',suppress_small=True)"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [],
"source": [
"VALUES = VALUES.split(\".\\n\")"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [],
"source": [
"OUTFILE = open(\"test-A/out.tsv\", \"w\")"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {},
"outputs": [],
"source": [
"for x in VALUES:\n",
" RESULT = x.replace(\" \",\"\").replace(\"[\",\"\").replace(\"]\",\"\")\n",
" OUTFILE.write(str(RESULT) )"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {},
"outputs": [],
"source": [
"OUTFILE.close()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.8.5"
}
},
"nbformat": 4,
"nbformat_minor": 4
}

1000
dev-0/out.tsv
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253
solution.ipynb
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@ -1,253 +0,0 @@
{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd\n",
"import numpy as np\n",
"from pathlib import Path\n",
"import seaborn as sns\n",
"import matplotlib.pyplot as plt\n",
"import numpy as np\n",
"from sklearn.linear_model import LinearRegression"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## TRENING"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"NAMES = [\"Price\",\"Mileage\",\"Year\",\"Brand\",\"EngineType\",\"EngineCapacity\"]\n",
"TRAIN_BASE = pd.read_csv(\"train/train.tsv\", sep ='\\t', names=NAMES)"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"Y_TRAIN = np.array(TRAIN_BASE[\"Price\"])\n",
"X_TRAIN = np.array(TRAIN_BASE[[\"Mileage\",\"Year\",\"EngineCapacity\"]])"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"MODEL = LinearRegression().fit(X_TRAIN,Y_TRAIN)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## DEV-0"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
"NAMES = [\"Mileage\",\"Year\",\"Brand\",\"EngineType\",\"EngineCapacity\"]\n",
"FILE_BASE = pd.read_csv(\"dev-0/in.tsv\", sep ='\\t', names=NAMES)"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [],
"source": [
"X_TEST = np.array(FILE_BASE[[\"Mileage\",\"Year\",\"EngineCapacity\"]])"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [],
"source": [
"Y_TEST = MODEL.predict(X_TEST)"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [],
"source": [
"VALUES = np.array2string(Y_TEST, precision=5, separator='\\n',suppress_small=True)"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [],
"source": [
"VALUES = VALUES.split(\".\\n\")"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [],
"source": [
"OUTFILE = open(\"dev-0/out.tsv\", \"w\")"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"scrolled": false
},
"outputs": [],
"source": [
"for x in VALUES:\n",
" RESULT = x.replace(\" \",\"\")\n",
" RESULT = RESULT.replace(\"[\",\"\")\n",
" RESULT = RESULT.replace(\"]\",\"\")\n",
" OUTFILE.write(str(RESULT))"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [],
"source": [
"OUTFILE.close()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## TEST A"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [],
"source": [
"NAMES = [\"Mileage\",\"Year\",\"Brand\",\"EngineType\",\"EngineCapacity\"]\n",
"FILE_BASE = pd.read_csv(\"test-A/in.tsv\", sep ='\\t', names=NAMES)"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [],
"source": [
"X_TEST = np.array(FILE_BASE[[\"Mileage\",\"Year\",\"EngineCapacity\"]])"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [],
"source": [
"Y_TEST = MODEL.predict(X_TEST)"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [],
"source": [
"VALUES = np.array2string(Y_TEST, precision=5, separator='\\n',suppress_small=True)"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [],
"source": [
"VALUES = VALUES.split(\".\\n\")"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [],
"source": [
"OUTFILE = open(\"test-A/out.tsv\", \"w\")"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {},
"outputs": [],
"source": [
"for x in VALUES:\n",
" RESULT = x.replace(\" \",\"\")\n",
" RESULT = RESULT.replace(\"[\",\"\")\n",
" RESULT = RESULT.replace(\"]\",\"\")\n",
" OUTFILE.write(str(RESULT) )"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {},
"outputs": [],
"source": [
"OUTFILE.close()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.8.5"
}
},
"nbformat": 4,
"nbformat_minor": 4
}

152
solution.py
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@ -1,152 +0,0 @@
#!/usr/bin/env python
# coding: utf-8
# In[1]:
import pandas as pd
import numpy as np
from pathlib import Path
import seaborn as sns
import matplotlib.pyplot as plt
import numpy as np
from sklearn.linear_model import LinearRegression
# ## TRENING
# In[2]:
NAMES = ["Price","Mileage","Year","Brand","EngineType","EngineCapacity"]
TRAIN_BASE = pd.read_csv("train/train.tsv", sep ='\t', names=NAMES)
# In[3]:
Y_TRAIN = np.array(TRAIN_BASE["Price"])
X_TRAIN = np.array(TRAIN_BASE[["Mileage","Year","EngineCapacity"]])
# In[4]:
MODEL = LinearRegression().fit(X_TRAIN,Y_TRAIN)
# ## DEV-0
# In[5]:
NAMES = ["Mileage","Year","Brand","EngineType","EngineCapacity"]
FILE_BASE = pd.read_csv("dev-0/in.tsv", sep ='\t', names=NAMES)
# In[6]:
X_TEST = np.array(FILE_BASE[["Mileage","Year","EngineCapacity"]])
# In[7]:
Y_TEST = MODEL.predict(X_TEST)
# In[8]:
VALUES = np.array2string(Y_TEST, precision=5, separator='\n',suppress_small=True)
# In[9]:
VALUES = VALUES.split(".\n")
# In[10]:
OUTFILE = open("dev-0/out.tsv", "w")
# In[11]:
for x in VALUES:
RESULT = x.replace(" ","")
RESULT = RESULT.replace("[","")
RESULT = RESULT.replace("]","")
OUTFILE.write(str(RESULT))
# In[12]:
OUTFILE.close()
# ## TEST A
# In[13]:
NAMES = ["Mileage","Year","Brand","EngineType","EngineCapacity"]
FILE_BASE = pd.read_csv("test-A/in.tsv", sep ='\t', names=NAMES)
# In[14]:
X_TEST = np.array(FILE_BASE[["Mileage","Year","EngineCapacity"]])
# In[15]:
Y_TEST = MODEL.predict(X_TEST)
# In[16]:
VALUES = np.array2string(Y_TEST, precision=5, separator='\n',suppress_small=True)
# In[17]:
VALUES = VALUES.split(".\n")
# In[18]:
OUTFILE = open("test-A/out.tsv", "w")
# In[19]:
for x in VALUES:
RESULT = x.replace(" ","")
RESULT = RESULT.replace("[","")
RESULT = RESULT.replace("]","")
OUTFILE.write(str(RESULT) )
# In[20]:
OUTFILE.close()
# In[ ]:

1000
test-A/out.tsv
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