mirror of
https://github.com/BlackWallTeam/ML.git
synced 2024-11-23 16:13:44 +03:00
2404 lines
112 KiB
Plaintext
2404 lines
112 KiB
Plaintext
{
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"import pandas as pd\n",
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"df = pd.read_excel('/content/krasnodar_hack_data.xlsx')\n",
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"df['date'] = df['date'].apply(lambda x: x[1:-1])\n",
|
||
"df['date'] = pd.to_datetime(df['date'])\n",
|
||
"df['floor'] = df['floor'].apply(lambda x: str(x).replace('--', '-'))\n",
|
||
"df['liter_num'] = df['liter_num'].apply(lambda x: str(x).replace('--', '-'))\n",
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||
"df['price_per_m'] = df['price_per_m'].apply(lambda x: x.replace('\\xa0', '').replace(' ', '')).astype(int)\n",
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"df['num_beds'] = df['num_beds'].astype(int)\n",
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"df"
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" community liter_num done_date developer floor \\\n",
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"0 ЖК «Смородина» 1-5 1 квартал 2023 ООО «АЛЬФА» 1-4 \n",
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"2 ЖК «Смородина» 1-5 1 квартал 2023 ООО «АЛЬФА» 9-12 \n",
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"3 ЖК «Смородина» 1-5 1 квартал 2023 ООО «АЛЬФА» 13-16 \n",
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"4 ЖК «Смородина» 1-5 1 квартал 2023 ООО «АЛЬФА» 1-4 \n",
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"... ... ... ... ... ... \n",
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||
"1284 ЖК «Role Clef» nan 2 полугодие 2021 ООО «Уютный дом» 2-12 \n",
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||
"1285 ЖК «Role Clef» nan 2 полугодие 2021 ООО «Уютный дом» 2-12 \n",
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||
"1286 ЖК «Role Clef» nan 2 полугодие 2021 ООО «Уютный дом» 2-12 \n",
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"1287 ЖК «Мелодия» nan дом сдан NaN 5 \n",
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},
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{
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"cell_type": "code",
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"source": [
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],
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"metadata": {
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"id": "v7B0OICi8LOx"
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{
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"cell_type": "code",
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"source": [
|
||
"df = pd.read_csv('/content/drive/MyDrive/krasnodar_hack/krasnodar_data_final.csv')\n",
|
||
"df = df[~df['area'].isna()]\n",
|
||
"df = df[~df['done_date'].isna()]\n"
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"metadata": {
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"df_uniq = df.drop(columns=['price_per_m', 'date', 'floor']).drop_duplicates() #.to_csv('unique_communities.csv', index=False)"
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{
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"cell_type": "code",
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"source": [
|
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"t = df.groupby(['community', 'liter_num', 'done_date', 'developer', 'area', 'num_beds', 'date'])['price_per_m'].mean()"
|
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],
|
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"metadata": {
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|
||
"metadata": {
|
||
"id": "344wp42UpZwo"
|
||
},
|
||
"execution_count": null,
|
||
"outputs": []
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"source": [
|
||
"t.community.value_counts()"
|
||
],
|
||
"metadata": {
|
||
"colab": {
|
||
"base_uri": "https://localhost:8080/"
|
||
},
|
||
"id": "PE9rV5olp36f",
|
||
"outputId": "64bfe96a-47ee-43f9-8de9-a9c768444595"
|
||
},
|
||
"execution_count": null,
|
||
"outputs": [
|
||
{
|
||
"output_type": "execute_result",
|
||
"data": {
|
||
"text/plain": [
|
||
"ЖК «Novella» 53\n",
|
||
"ЖК «Зеленодар» 47\n",
|
||
"ЖК «AVrorA», 42\n",
|
||
"ЖК «Смородина» 40\n",
|
||
"ЖК «Fresh» 39\n",
|
||
"ЖК «The Grand Palace» 21\n",
|
||
"ЖК «Небо» 20\n",
|
||
"ЖК «URAL», 18\n",
|
||
"ЖК «Спортивный парк» 17\n",
|
||
"ЖК «Дом на Лаврова» 8\n",
|
||
"Name: community, dtype: int64"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"execution_count": 144
|
||
}
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"source": [
|
||
"rep = {\n",
|
||
" '4 квартал 2021' : \"4 квартал 2021\",\n",
|
||
" '9 квартал 2021' : \"4 квартал 2021\",\n",
|
||
" '8 квартал 2021' : \"4 квартал 2021\",\n",
|
||
" '7 квартал 2021' : \"4 квартал 2021\",\n",
|
||
" '6 квартал 2021' : \"4 квартал 2021\",\n",
|
||
" '5 квартал 2021' : \"4 квартал 2021\",\n",
|
||
" '18 квартал 2021' : \"4 квартал 2021\",\n",
|
||
" '17 квартал 2021' : \"4 квартал 2021\",\n",
|
||
" '16 квартал 2021' : \"4 квартал 2021\",\n",
|
||
" '14 квартал 2021' : \"4 квартал 2021\",\n",
|
||
" '13 квартал 2021' : \"4 квартал 2021\",\n",
|
||
" '12 квартал 2021' : \"4 квартал 2021\",\n",
|
||
" '11 квартал 2021' : \"4 квартал 2021\",\n",
|
||
" '10 квартал 2021' : \"4 квартал 2021\",\n",
|
||
" '15 квартал 2021' : \"4 квартал 2021\",\n",
|
||
"}\n",
|
||
"kvartal_to_date = {\n",
|
||
" '4 квартал 2021': '2021-12-15',\n",
|
||
" '2 квартал 2022': '2022-06-15',\n",
|
||
" '3 квартал 2022': '2022-09-15',\n",
|
||
" '1 квартал 2023': '2023-03-15',\n",
|
||
" '2 квартал 2023': '2023-06-15',\n",
|
||
" '3 квартал 2023': '2023-09-15',\n",
|
||
" '4 квартал 2023': '2023-12-15',\n",
|
||
" '1 квартал 2024': '2024-03-15',\n",
|
||
" '2 квартал 2024': '2024-06-15',\n",
|
||
" '3 квартал 2024': '2024-09-15',\n",
|
||
" '4 квартал 2024': '2024-12-15',\n",
|
||
" 'дом сдан': '1990-12-15'\n",
|
||
"}"
|
||
],
|
||
"metadata": {
|
||
"id": "OjfuBFcqs1pa"
|
||
},
|
||
"execution_count": null,
|
||
"outputs": []
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"source": [
|
||
"t['done_date'] = t.done_date.replace(rep)"
|
||
],
|
||
"metadata": {
|
||
"id": "HnG-tuphqo29"
|
||
},
|
||
"execution_count": null,
|
||
"outputs": []
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"source": [],
|
||
"metadata": {
|
||
"id": "Hr0tYXDnq7Xx"
|
||
},
|
||
"execution_count": null,
|
||
"outputs": []
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"source": [
|
||
"t.done_date = t.done_date.replace(kvartal_to_date)"
|
||
],
|
||
"metadata": {
|
||
"id": "1YCU5SSkuXMB"
|
||
},
|
||
"execution_count": null,
|
||
"outputs": []
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"source": [
|
||
"t.done_date = pd.to_datetime(t.done_date)\n",
|
||
"t.date = pd.to_datetime(t.date)\n",
|
||
"t['days_to_done'] = t['done_date'] - t['date']\n",
|
||
"t['days_to_done'] = t['days_to_done'].apply(lambda x: int(str(x).split()[0]))\n"
|
||
],
|
||
"metadata": {
|
||
"id": "LaxUGtDRubue"
|
||
},
|
||
"execution_count": null,
|
||
"outputs": []
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"source": [],
|
||
"metadata": {
|
||
"id": "az33D3T0u1Fe"
|
||
},
|
||
"execution_count": null,
|
||
"outputs": []
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"source": [],
|
||
"metadata": {
|
||
"id": "xJxBzp7Bwe2i"
|
||
},
|
||
"execution_count": null,
|
||
"outputs": []
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"source": [
|
||
"trend = []\n",
|
||
"for idx, row in t.iterrows():\n",
|
||
" if row.date == pd.to_datetime('2022-01-25'):\n",
|
||
" trend.append(0)\n",
|
||
" continue\n",
|
||
" elif row.date == pd.to_datetime('2022-11-06') and row.area == t.iloc[idx-1].area:\n",
|
||
" trend.append(row.price_per_m/(t.iloc[idx-1].price_per_m)*100)\n",
|
||
" continue\n",
|
||
" elif row.date == pd.to_datetime('2023-02-15') and row.area == t.iloc[idx-1].area:\n",
|
||
" trend.append(row.price_per_m/(t.iloc[idx-2].price_per_m)*100)\n",
|
||
" continue\n",
|
||
" else:\n",
|
||
" trend.append(0)"
|
||
],
|
||
"metadata": {
|
||
"id": "yGCs4h8V1izN"
|
||
},
|
||
"execution_count": null,
|
||
"outputs": []
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"source": [
|
||
"t['trend'] = trend"
|
||
],
|
||
"metadata": {
|
||
"id": "B6LkDXQ123BG"
|
||
},
|
||
"execution_count": null,
|
||
"outputs": []
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"source": [
|
||
"t['trend'] = t.trend.apply(lambda x: round(x, 2))"
|
||
],
|
||
"metadata": {
|
||
"id": "65q_WpTE24zn"
|
||
},
|
||
"execution_count": null,
|
||
"outputs": []
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"source": [
|
||
"t = t[~((t['trend'] < 100) & (t['trend'] > 50))]"
|
||
],
|
||
"metadata": {
|
||
"id": "MY0wDX8s388F"
|
||
},
|
||
"execution_count": null,
|
||
"outputs": []
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"source": [
|
||
"same_cols = ['community', 'liter_num', 'done_date', 'developer', 'area', 'num_beds']"
|
||
],
|
||
"metadata": {
|
||
"id": "3ZviqlKJHHga"
|
||
},
|
||
"execution_count": null,
|
||
"outputs": []
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"source": [
|
||
"new_df = []\n",
|
||
"for idx, row in t.iterrows():\n",
|
||
" if row.date == pd.to_datetime('2022-01-25'):\n",
|
||
" i = 1\n",
|
||
" if idx+i < t.shape[0]:\n",
|
||
" if (t.iloc[idx+i][same_cols] == row[same_cols]).values.sum() == 6:\n",
|
||
" row['price_per_m'] = t.iloc[idx+i]['price_per_m']\n",
|
||
" row['trend'] = t.iloc[idx+i]['trend']\n",
|
||
"\n",
|
||
" new_df.append(row)\n",
|
||
" if row.date == pd.to_datetime('2022-11-06'):\n",
|
||
" i = 1\n",
|
||
" if idx+i < t.shape[0]:\n",
|
||
" if (t.iloc[idx+i][same_cols] == row[same_cols]).values.sum() == 6:\n",
|
||
" row['price_per_m'] = t.iloc[idx+i]['price_per_m']\n",
|
||
" row['trend'] = t.iloc[idx+i]['trend']\n",
|
||
"\n",
|
||
" new_df.append(row)"
|
||
],
|
||
"metadata": {
|
||
"id": "93JSoKBK941f"
|
||
},
|
||
"execution_count": null,
|
||
"outputs": []
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"source": [
|
||
"new_df = pd.DataFrame(new_df)"
|
||
],
|
||
"metadata": {
|
||
"id": "EqxOuJDpH5OG"
|
||
},
|
||
"execution_count": null,
|
||
"outputs": []
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"source": [
|
||
"new_df = new_df[~(new_df['trend'] == 0)]"
|
||
],
|
||
"metadata": {
|
||
"id": "Nu_gJz4cIDAf"
|
||
},
|
||
"execution_count": null,
|
||
"outputs": []
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"source": [
|
||
"t"
|
||
],
|
||
"metadata": {
|
||
"colab": {
|
||
"base_uri": "https://localhost:8080/",
|
||
"height": 423
|
||
},
|
||
"id": "rgQgfyA3ItLS",
|
||
"outputId": "7ce259e9-638d-4317-db58-eb8fed976978"
|
||
},
|
||
"execution_count": null,
|
||
"outputs": [
|
||
{
|
||
"output_type": "execute_result",
|
||
"data": {
|
||
"text/plain": [
|
||
" community liter_num done_date developer \\\n",
|
||
"0 ЖК «AVrorA», nan 2022-09-15 ООО «АльфаСтройКомплекс» \n",
|
||
"1 ЖК «AVrorA», nan 2022-09-15 ООО «АльфаСтройКомплекс» \n",
|
||
"2 ЖК «AVrorA», nan 2022-09-15 ООО «АльфаСтройКомплекс» \n",
|
||
"3 ЖК «AVrorA», nan 2022-09-15 ООО «АльфаСтройКомплекс» \n",
|
||
"4 ЖК «AVrorA», nan 2022-09-15 ООО «АльфаСтройКомплекс» \n",
|
||
".. ... ... ... ... \n",
|
||
"300 ЖК «Спортивный парк» 7 2022-06-15 ООО «АЛЬФА» \n",
|
||
"301 ЖК «Спортивный парк» 8 2022-06-15 ООО «АЛЬФА» \n",
|
||
"302 ЖК «Спортивный парк» 8 2022-06-15 ООО «АЛЬФА» \n",
|
||
"303 ЖК «Спортивный парк» 8 2022-06-15 ООО «АЛЬФА» \n",
|
||
"304 ЖК «Спортивный парк» 8 2022-06-15 ООО «АЛЬФА» \n",
|
||
"\n",
|
||
" area num_beds date price_per_m days_to_done trend \n",
|
||
"0 44.0 1 2022-01-25 187000.0 233 0.00 \n",
|
||
"1 44.0 1 2022-11-06 202000.0 -52 108.02 \n",
|
||
"2 44.0 1 2023-02-15 236600.0 -153 126.52 \n",
|
||
"3 47.0 1 2022-01-25 200750.0 233 0.00 \n",
|
||
"4 47.0 1 2022-11-06 205750.0 -52 102.49 \n",
|
||
".. ... ... ... ... ... ... \n",
|
||
"300 45.0 1 2023-02-15 101800.0 -245 117.01 \n",
|
||
"301 48.0 1 2022-01-25 84000.0 141 0.00 \n",
|
||
"302 48.0 1 2022-11-06 98000.0 -144 116.67 \n",
|
||
"303 51.0 1 2022-01-25 79000.0 141 0.00 \n",
|
||
"304 51.0 1 2022-11-06 92500.0 -144 117.09 \n",
|
||
"\n",
|
||
"[300 rows x 10 columns]"
|
||
],
|
||
"text/html": [
|
||
"\n",
|
||
" <div id=\"df-81f96bdc-7acd-49f1-9d7b-1e74dd5c623f\">\n",
|
||
" <div class=\"colab-df-container\">\n",
|
||
" <div>\n",
|
||
"<style scoped>\n",
|
||
" .dataframe tbody tr th:only-of-type {\n",
|
||
" vertical-align: middle;\n",
|
||
" }\n",
|
||
"\n",
|
||
" .dataframe tbody tr th {\n",
|
||
" vertical-align: top;\n",
|
||
" }\n",
|
||
"\n",
|
||
" .dataframe thead th {\n",
|
||
" text-align: right;\n",
|
||
" }\n",
|
||
"</style>\n",
|
||
"<table border=\"1\" class=\"dataframe\">\n",
|
||
" <thead>\n",
|
||
" <tr style=\"text-align: right;\">\n",
|
||
" <th></th>\n",
|
||
" <th>community</th>\n",
|
||
" <th>liter_num</th>\n",
|
||
" <th>done_date</th>\n",
|
||
" <th>developer</th>\n",
|
||
" <th>area</th>\n",
|
||
" <th>num_beds</th>\n",
|
||
" <th>date</th>\n",
|
||
" <th>price_per_m</th>\n",
|
||
" <th>days_to_done</th>\n",
|
||
" <th>trend</th>\n",
|
||
" </tr>\n",
|
||
" </thead>\n",
|
||
" <tbody>\n",
|
||
" <tr>\n",
|
||
" <th>0</th>\n",
|
||
" <td>ЖК «AVrorA»,</td>\n",
|
||
" <td>nan</td>\n",
|
||
" <td>2022-09-15</td>\n",
|
||
" <td>ООО «АльфаСтройКомплекс»</td>\n",
|
||
" <td>44.0</td>\n",
|
||
" <td>1</td>\n",
|
||
" <td>2022-01-25</td>\n",
|
||
" <td>187000.0</td>\n",
|
||
" <td>233</td>\n",
|
||
" <td>0.00</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>1</th>\n",
|
||
" <td>ЖК «AVrorA»,</td>\n",
|
||
" <td>nan</td>\n",
|
||
" <td>2022-09-15</td>\n",
|
||
" <td>ООО «АльфаСтройКомплекс»</td>\n",
|
||
" <td>44.0</td>\n",
|
||
" <td>1</td>\n",
|
||
" <td>2022-11-06</td>\n",
|
||
" <td>202000.0</td>\n",
|
||
" <td>-52</td>\n",
|
||
" <td>108.02</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>2</th>\n",
|
||
" <td>ЖК «AVrorA»,</td>\n",
|
||
" <td>nan</td>\n",
|
||
" <td>2022-09-15</td>\n",
|
||
" <td>ООО «АльфаСтройКомплекс»</td>\n",
|
||
" <td>44.0</td>\n",
|
||
" <td>1</td>\n",
|
||
" <td>2023-02-15</td>\n",
|
||
" <td>236600.0</td>\n",
|
||
" <td>-153</td>\n",
|
||
" <td>126.52</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>3</th>\n",
|
||
" <td>ЖК «AVrorA»,</td>\n",
|
||
" <td>nan</td>\n",
|
||
" <td>2022-09-15</td>\n",
|
||
" <td>ООО «АльфаСтройКомплекс»</td>\n",
|
||
" <td>47.0</td>\n",
|
||
" <td>1</td>\n",
|
||
" <td>2022-01-25</td>\n",
|
||
" <td>200750.0</td>\n",
|
||
" <td>233</td>\n",
|
||
" <td>0.00</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>4</th>\n",
|
||
" <td>ЖК «AVrorA»,</td>\n",
|
||
" <td>nan</td>\n",
|
||
" <td>2022-09-15</td>\n",
|
||
" <td>ООО «АльфаСтройКомплекс»</td>\n",
|
||
" <td>47.0</td>\n",
|
||
" <td>1</td>\n",
|
||
" <td>2022-11-06</td>\n",
|
||
" <td>205750.0</td>\n",
|
||
" <td>-52</td>\n",
|
||
" <td>102.49</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>...</th>\n",
|
||
" <td>...</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>...</td>\n",
|
||
" <td>...</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>300</th>\n",
|
||
" <td>ЖК «Спортивный парк»</td>\n",
|
||
" <td>7</td>\n",
|
||
" <td>2022-06-15</td>\n",
|
||
" <td>ООО «АЛЬФА»</td>\n",
|
||
" <td>45.0</td>\n",
|
||
" <td>1</td>\n",
|
||
" <td>2023-02-15</td>\n",
|
||
" <td>101800.0</td>\n",
|
||
" <td>-245</td>\n",
|
||
" <td>117.01</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>301</th>\n",
|
||
" <td>ЖК «Спортивный парк»</td>\n",
|
||
" <td>8</td>\n",
|
||
" <td>2022-06-15</td>\n",
|
||
" <td>ООО «АЛЬФА»</td>\n",
|
||
" <td>48.0</td>\n",
|
||
" <td>1</td>\n",
|
||
" <td>2022-01-25</td>\n",
|
||
" <td>84000.0</td>\n",
|
||
" <td>141</td>\n",
|
||
" <td>0.00</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>302</th>\n",
|
||
" <td>ЖК «Спортивный парк»</td>\n",
|
||
" <td>8</td>\n",
|
||
" <td>2022-06-15</td>\n",
|
||
" <td>ООО «АЛЬФА»</td>\n",
|
||
" <td>48.0</td>\n",
|
||
" <td>1</td>\n",
|
||
" <td>2022-11-06</td>\n",
|
||
" <td>98000.0</td>\n",
|
||
" <td>-144</td>\n",
|
||
" <td>116.67</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>303</th>\n",
|
||
" <td>ЖК «Спортивный парк»</td>\n",
|
||
" <td>8</td>\n",
|
||
" <td>2022-06-15</td>\n",
|
||
" <td>ООО «АЛЬФА»</td>\n",
|
||
" <td>51.0</td>\n",
|
||
" <td>1</td>\n",
|
||
" <td>2022-01-25</td>\n",
|
||
" <td>79000.0</td>\n",
|
||
" <td>141</td>\n",
|
||
" <td>0.00</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>304</th>\n",
|
||
" <td>ЖК «Спортивный парк»</td>\n",
|
||
" <td>8</td>\n",
|
||
" <td>2022-06-15</td>\n",
|
||
" <td>ООО «АЛЬФА»</td>\n",
|
||
" <td>51.0</td>\n",
|
||
" <td>1</td>\n",
|
||
" <td>2022-11-06</td>\n",
|
||
" <td>92500.0</td>\n",
|
||
" <td>-144</td>\n",
|
||
" <td>117.09</td>\n",
|
||
" </tr>\n",
|
||
" </tbody>\n",
|
||
"</table>\n",
|
||
"<p>300 rows × 10 columns</p>\n",
|
||
"</div>\n",
|
||
" <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-81f96bdc-7acd-49f1-9d7b-1e74dd5c623f')\"\n",
|
||
" title=\"Convert this dataframe to an interactive table.\"\n",
|
||
" style=\"display:none;\">\n",
|
||
" \n",
|
||
" <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
|
||
" width=\"24px\">\n",
|
||
" <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
|
||
" <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
|
||
" </svg>\n",
|
||
" </button>\n",
|
||
" \n",
|
||
" <style>\n",
|
||
" .colab-df-container {\n",
|
||
" display:flex;\n",
|
||
" flex-wrap:wrap;\n",
|
||
" gap: 12px;\n",
|
||
" }\n",
|
||
"\n",
|
||
" .colab-df-convert {\n",
|
||
" background-color: #E8F0FE;\n",
|
||
" border: none;\n",
|
||
" border-radius: 50%;\n",
|
||
" cursor: pointer;\n",
|
||
" display: none;\n",
|
||
" fill: #1967D2;\n",
|
||
" height: 32px;\n",
|
||
" padding: 0 0 0 0;\n",
|
||
" width: 32px;\n",
|
||
" }\n",
|
||
"\n",
|
||
" .colab-df-convert:hover {\n",
|
||
" background-color: #E2EBFA;\n",
|
||
" box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
|
||
" fill: #174EA6;\n",
|
||
" }\n",
|
||
"\n",
|
||
" [theme=dark] .colab-df-convert {\n",
|
||
" background-color: #3B4455;\n",
|
||
" fill: #D2E3FC;\n",
|
||
" }\n",
|
||
"\n",
|
||
" [theme=dark] .colab-df-convert:hover {\n",
|
||
" background-color: #434B5C;\n",
|
||
" box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
|
||
" filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
|
||
" fill: #FFFFFF;\n",
|
||
" }\n",
|
||
" </style>\n",
|
||
"\n",
|
||
" <script>\n",
|
||
" const buttonEl =\n",
|
||
" document.querySelector('#df-81f96bdc-7acd-49f1-9d7b-1e74dd5c623f button.colab-df-convert');\n",
|
||
" buttonEl.style.display =\n",
|
||
" google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
|
||
"\n",
|
||
" async function convertToInteractive(key) {\n",
|
||
" const element = document.querySelector('#df-81f96bdc-7acd-49f1-9d7b-1e74dd5c623f');\n",
|
||
" const dataTable =\n",
|
||
" await google.colab.kernel.invokeFunction('convertToInteractive',\n",
|
||
" [key], {});\n",
|
||
" if (!dataTable) return;\n",
|
||
"\n",
|
||
" const docLinkHtml = 'Like what you see? Visit the ' +\n",
|
||
" '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
|
||
" + ' to learn more about interactive tables.';\n",
|
||
" element.innerHTML = '';\n",
|
||
" dataTable['output_type'] = 'display_data';\n",
|
||
" await google.colab.output.renderOutput(dataTable, element);\n",
|
||
" const docLink = document.createElement('div');\n",
|
||
" docLink.innerHTML = docLinkHtml;\n",
|
||
" element.appendChild(docLink);\n",
|
||
" }\n",
|
||
" </script>\n",
|
||
" </div>\n",
|
||
" </div>\n",
|
||
" "
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"execution_count": 358
|
||
}
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"source": [
|
||
"t['date'].unique()"
|
||
],
|
||
"metadata": {
|
||
"colab": {
|
||
"base_uri": "https://localhost:8080/"
|
||
},
|
||
"id": "7vWFl3uoIMnm",
|
||
"outputId": "1d5dbc94-c32c-4426-a324-4a6186f44773"
|
||
},
|
||
"execution_count": null,
|
||
"outputs": [
|
||
{
|
||
"output_type": "execute_result",
|
||
"data": {
|
||
"text/plain": [
|
||
"array(['2022-01-25T00:00:00.000000000', '2022-11-06T00:00:00.000000000',\n",
|
||
" '2023-02-15T00:00:00.000000000'], dtype='datetime64[ns]')"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"execution_count": 365
|
||
}
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"source": [
|
||
"test_df = new_df[['area', 'num_beds', 'price_per_m', 'days_to_done', 'community']]\n",
|
||
"test_df = test_df[test_df['days_to_done'] > -2000]\n",
|
||
"test_df['community'] = test_df['community'].apply(lambda x: x.strip())"
|
||
],
|
||
"metadata": {
|
||
"id": "GsddQYnB_hi5"
|
||
},
|
||
"execution_count": null,
|
||
"outputs": []
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"source": [
|
||
"from sklearn.linear_model import LinearRegression\n",
|
||
"from sklearn.model_selection import StratifiedKFold"
|
||
],
|
||
"metadata": {
|
||
"id": "iIV_dc5N_nZB"
|
||
},
|
||
"execution_count": null,
|
||
"outputs": []
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"source": [
|
||
"kfold = StratifiedKFold(n_splits=5, shuffle=True, random_state=0xBACED)"
|
||
],
|
||
"metadata": {
|
||
"id": "g-1NcmetADYQ"
|
||
},
|
||
"execution_count": null,
|
||
"outputs": []
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"source": [
|
||
"for fold, (train_index, val_index) in enumerate(kfold.split(test_df, test_df['days_to_done'])):\n",
|
||
" if fold == 0:\n",
|
||
" df_train = test_df.iloc[train_index]\n",
|
||
" df_val = test_df.iloc[val_index]\n"
|
||
],
|
||
"metadata": {
|
||
"colab": {
|
||
"base_uri": "https://localhost:8080/"
|
||
},
|
||
"id": "QbgRtLQ-AI89",
|
||
"outputId": "18b36f27-7059-470d-eb7a-5e7e1c0e03db"
|
||
},
|
||
"execution_count": null,
|
||
"outputs": [
|
||
{
|
||
"output_type": "stream",
|
||
"name": "stderr",
|
||
"text": [
|
||
"/usr/local/lib/python3.8/dist-packages/sklearn/model_selection/_split.py:676: UserWarning: The least populated class in y has only 1 members, which is less than n_splits=5.\n",
|
||
" warnings.warn(\n"
|
||
]
|
||
}
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"source": [
|
||
"X = df_train.drop(columns=['price_per_m'])\n",
|
||
"y = df_train['price_per_m'].values\n",
|
||
"\n",
|
||
"X_val = df_val.drop(columns=['price_per_m'])\n",
|
||
"y_val = df_val['price_per_m'].values"
|
||
],
|
||
"metadata": {
|
||
"id": "asPYq7hz_uAR"
|
||
},
|
||
"execution_count": null,
|
||
"outputs": []
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"source": [
|
||
"!pip install shap catboost -qqq"
|
||
],
|
||
"metadata": {
|
||
"colab": {
|
||
"base_uri": "https://localhost:8080/"
|
||
},
|
||
"id": "clgz4UO8A92Y",
|
||
"outputId": "5f8340de-1a3d-4f02-b895-0c64e09adfa0"
|
||
},
|
||
"execution_count": null,
|
||
"outputs": [
|
||
{
|
||
"output_type": "stream",
|
||
"name": "stdout",
|
||
"text": [
|
||
"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m575.9/575.9 KB\u001b[0m \u001b[31m12.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
|
||
"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m76.6/76.6 MB\u001b[0m \u001b[31m12.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
|
||
"\u001b[?25h"
|
||
]
|
||
}
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"source": [
|
||
"from catboost import CatBoostRegressor, Pool"
|
||
],
|
||
"metadata": {
|
||
"id": "060Gc1XtCYYA"
|
||
},
|
||
"execution_count": null,
|
||
"outputs": []
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"source": [
|
||
"regression_params = dict(\n",
|
||
" iterations=5000, \n",
|
||
" learning_rate=0.05,\n",
|
||
" verbose=100,\n",
|
||
" early_stopping_rounds=500,\n",
|
||
" loss_function='RMSE',\n",
|
||
" eval_metric='MAE',\n",
|
||
" task_type='CPU',\n",
|
||
" cat_features=['community']\n",
|
||
")\n"
|
||
],
|
||
"metadata": {
|
||
"id": "PRyAwOaTCjw1"
|
||
},
|
||
"execution_count": null,
|
||
"outputs": []
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"source": [
|
||
"model = CatBoostRegressor(**regression_params).fit(X, y, eval_set=(X_val, y_val))"
|
||
],
|
||
"metadata": {
|
||
"colab": {
|
||
"base_uri": "https://localhost:8080/"
|
||
},
|
||
"id": "vSL8GlhyCZ2n",
|
||
"outputId": "ce8a65b1-edaf-44b3-bf69-e3c70febb28b"
|
||
},
|
||
"execution_count": null,
|
||
"outputs": [
|
||
{
|
||
"output_type": "stream",
|
||
"name": "stdout",
|
||
"text": [
|
||
"0:\tlearn: 37709.1413245\ttest: 37499.9541219\tbest: 37499.9541219 (0)\ttotal: 848us\tremaining: 4.24s\n",
|
||
"100:\tlearn: 9375.1949588\ttest: 8095.7973147\tbest: 8082.1061214 (99)\ttotal: 60.6ms\tremaining: 2.94s\n",
|
||
"200:\tlearn: 6081.2680389\ttest: 6465.3010380\tbest: 6465.3010380 (200)\ttotal: 185ms\tremaining: 4.41s\n",
|
||
"300:\tlearn: 4427.3214133\ttest: 6545.6179437\tbest: 6361.4411869 (251)\ttotal: 397ms\tremaining: 6.19s\n",
|
||
"400:\tlearn: 3395.6664363\ttest: 6681.1132311\tbest: 6361.4411869 (251)\ttotal: 551ms\tremaining: 6.31s\n",
|
||
"500:\tlearn: 2712.4185987\ttest: 6611.7024042\tbest: 6361.4411869 (251)\ttotal: 732ms\tremaining: 6.58s\n",
|
||
"600:\tlearn: 2258.2942629\ttest: 6603.5144130\tbest: 6361.4411869 (251)\ttotal: 833ms\tremaining: 6.1s\n",
|
||
"700:\tlearn: 1912.3338542\ttest: 6570.2824488\tbest: 6361.4411869 (251)\ttotal: 993ms\tremaining: 6.09s\n",
|
||
"Stopped by overfitting detector (500 iterations wait)\n",
|
||
"\n",
|
||
"bestTest = 6361.441187\n",
|
||
"bestIteration = 251\n",
|
||
"\n",
|
||
"Shrink model to first 252 iterations.\n"
|
||
]
|
||
}
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"source": [
|
||
"from sklearn.metrics import mean_absolute_percentage_error"
|
||
],
|
||
"metadata": {
|
||
"id": "N7iYT6eBCfd9"
|
||
},
|
||
"execution_count": null,
|
||
"outputs": []
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"source": [
|
||
"y_pred = model.predict(X_val)\n",
|
||
"\n",
|
||
"1-mean_absolute_percentage_error(y_pred, y_val)"
|
||
],
|
||
"metadata": {
|
||
"colab": {
|
||
"base_uri": "https://localhost:8080/"
|
||
},
|
||
"id": "oFTHqEcPDflg",
|
||
"outputId": "f1fe7202-d1ed-49eb-abb1-7f114d7f44da"
|
||
},
|
||
"execution_count": null,
|
||
"outputs": [
|
||
{
|
||
"output_type": "execute_result",
|
||
"data": {
|
||
"text/plain": [
|
||
"0.9550957107941889"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"execution_count": 405
|
||
}
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"source": [
|
||
"y_val"
|
||
],
|
||
"metadata": {
|
||
"colab": {
|
||
"base_uri": "https://localhost:8080/"
|
||
},
|
||
"id": "-4SgdPmkLcja",
|
||
"outputId": "5dc0d00e-7dc0-44cb-df2d-35104cbb3e1b"
|
||
},
|
||
"execution_count": null,
|
||
"outputs": [
|
||
{
|
||
"output_type": "execute_result",
|
||
"data": {
|
||
"text/plain": [
|
||
"array([193000. , 226700. , 232200. , 219600. ,\n",
|
||
" 206000. , 202200. , 176583.33333333, 96000. ,\n",
|
||
" 117000. , 118200. , 129540. , 141500. ,\n",
|
||
" 128850. , 135500. , 135500. , 117500. ,\n",
|
||
" 113500. , 108500. , 186040. , 183666.66666667,\n",
|
||
" 106333.33333333, 131033.33333333, 115400. , 121500. ,\n",
|
||
" 109000. , 92500. ])"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"execution_count": 406
|
||
}
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"source": [
|
||
"y_pred"
|
||
],
|
||
"metadata": {
|
||
"colab": {
|
||
"base_uri": "https://localhost:8080/"
|
||
},
|
||
"id": "IeaNjO_oLcoZ",
|
||
"outputId": "ce1e60be-5e1b-4526-b99c-e42c1aa7a248"
|
||
},
|
||
"execution_count": null,
|
||
"outputs": [
|
||
{
|
||
"output_type": "execute_result",
|
||
"data": {
|
||
"text/plain": [
|
||
"array([202911.61234791, 236242.42223859, 233670.36797393, 210230.78031406,\n",
|
||
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|
||
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|
||
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|
||
" 115840.26418613, 109029.5771292 , 182236.07980921, 179040.32183855,\n",
|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
],
|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
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|
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|
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
"pd.DataFrame({'feature_importance': model.get_feature_importance(Pool(X, y, cat_features=['community'])), \n",
|
||
" 'feature_names': X.columns}).sort_values(by=['feature_importance'], \n",
|
||
" ascending=False)"
|
||
],
|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
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|
||
{
|
||
"output_type": "execute_result",
|
||
"data": {
|
||
"text/plain": [
|
||
" feature_importance feature_names\n",
|
||
"3 43.253428 community\n",
|
||
"2 20.491219 days_to_done\n",
|
||
"0 19.305202 area\n",
|
||
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|
||
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|
||
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|
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|
||
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|
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|
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
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|
||
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|
||
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|
||
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|
||
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|
||
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||
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|
||
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|
||
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||
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|
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|
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|
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|
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|
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|
||
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
||
"\n",
|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
"\n",
|
||
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|
||
" '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
|
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|
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|
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|
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|
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|
||
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|
||
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|
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
{
|
||
"cell_type": "code",
|
||
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|
||
"X_val.head()"
|
||
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|
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|
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|
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|
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|
||
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|
||
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|
||
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|
||
{
|
||
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|
||
"data": {
|
||
"text/plain": [
|
||
" area num_beds days_to_done community\n",
|
||
"6 49.0 1 233 ЖК «AVrorA», \n",
|
||
"7 49.0 1 -52 ЖК «AVrorA», \n",
|
||
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|
||
"22 67.0 2 -52 ЖК «AVrorA», \n",
|
||
"24 70.0 2 233 ЖК «AVrorA», "
|
||
],
|
||
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|
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|
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|
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|
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|
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
" <td>ЖК «AVrorA»,</td>\n",
|
||
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|
||
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|
||
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|
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|
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|
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|
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|
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|
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|
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|
||
" display:flex;\n",
|
||
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|
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|
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|
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|
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|
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|
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|
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|
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"\n",
|
||
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|
||
" background-color: #3B4455;\n",
|
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|
||
" }\n",
|
||
"\n",
|
||
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
||
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|
||
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|
||
"\n",
|
||
" const docLinkHtml = 'Like what you see? Visit the ' +\n",
|
||
" '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
|
||
" + ' to learn more about interactive tables.';\n",
|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
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|
||
" </div>\n",
|
||
" "
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"execution_count": 388
|
||
}
|
||
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|
||
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|
||
{
|
||
"cell_type": "code",
|
||
"source": [
|
||
"model.predict(X_val.iloc[0])"
|
||
],
|
||
"metadata": {
|
||
"colab": {
|
||
"base_uri": "https://localhost:8080/"
|
||
},
|
||
"id": "WV_8itnWLsuX",
|
||
"outputId": "396bf26b-37e2-46d5-9837-5f1ffdc5bfc3"
|
||
},
|
||
"execution_count": null,
|
||
"outputs": [
|
||
{
|
||
"output_type": "execute_result",
|
||
"data": {
|
||
"text/plain": [
|
||
"202911.6123479114"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"execution_count": 390
|
||
}
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"source": [
|
||
"model.predict(pd.DataFrame({'area': [44], 'num_beds': [1], 'days_to_done': [400], 'community':['ЖК «AVrorA»,']}))"
|
||
],
|
||
"metadata": {
|
||
"colab": {
|
||
"base_uri": "https://localhost:8080/"
|
||
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|
||
"id": "uRNrzco4N-FJ",
|
||
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||
},
|
||
"execution_count": null,
|
||
"outputs": [
|
||
{
|
||
"output_type": "execute_result",
|
||
"data": {
|
||
"text/plain": [
|
||
"array([197725.156463])"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"execution_count": 434
|
||
}
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"source": [
|
||
"import pickle\n",
|
||
"\n",
|
||
"with open('model_pervichka.pkl', 'wb') as f:\n",
|
||
" pickle.dump(model, f)"
|
||
],
|
||
"metadata": {
|
||
"id": "wBBuVC7QOSFL"
|
||
},
|
||
"execution_count": null,
|
||
"outputs": []
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"source": [
|
||
"d = test_df.drop(columns=['price_per_m']).drop_duplicates()"
|
||
],
|
||
"metadata": {
|
||
"id": "CKZW2i5YQAd-"
|
||
},
|
||
"execution_count": null,
|
||
"outputs": []
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"source": [
|
||
"d.num_beds.value_counts()"
|
||
],
|
||
"metadata": {
|
||
"colab": {
|
||
"base_uri": "https://localhost:8080/"
|
||
},
|
||
"id": "z5iZsIoeSm2P",
|
||
"outputId": "dc444da6-bb07-4796-e790-4538d532e9d5"
|
||
},
|
||
"execution_count": null,
|
||
"outputs": [
|
||
{
|
||
"output_type": "execute_result",
|
||
"data": {
|
||
"text/plain": [
|
||
"1 51\n",
|
||
"2 38\n",
|
||
"3 34\n",
|
||
"0 2\n",
|
||
"Name: num_beds, dtype: int64"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"execution_count": 441
|
||
}
|
||
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|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"source": [
|
||
"в"
|
||
],
|
||
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|
||
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|
||
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||
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||
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||
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|
||
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|
||
"execution_count": null,
|
||
"outputs": [
|
||
{
|
||
"output_type": "execute_result",
|
||
"data": {
|
||
"text/plain": [
|
||
" area num_beds days_to_done community\n",
|
||
"0 44.0 1 233 ЖК «AVrorA»,\n",
|
||
"1 44.0 1 -52 ЖК «AVrorA»,\n",
|
||
"3 47.0 1 233 ЖК «AVrorA»,\n",
|
||
"4 47.0 1 -52 ЖК «AVrorA»,\n",
|
||
"6 49.0 1 233 ЖК «AVrorA»,\n",
|
||
".. ... ... ... ...\n",
|
||
"293 45.0 1 -52 ЖК «Спортивный парк»\n",
|
||
"297 41.0 1 -144 ЖК «Спортивный парк»\n",
|
||
"299 45.0 1 -144 ЖК «Спортивный парк»\n",
|
||
"302 48.0 1 -144 ЖК «Спортивный парк»\n",
|
||
"304 51.0 1 -144 ЖК «Спортивный парк»\n",
|
||
"\n",
|
||
"[125 rows x 4 columns]"
|
||
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||
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||
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||
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|
||
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||
"<style scoped>\n",
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||
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|
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|
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"</style>\n",
|
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|
||
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|
||
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|
||
" <th></th>\n",
|
||
" <th>area</th>\n",
|
||
" <th>num_beds</th>\n",
|
||
" <th>days_to_done</th>\n",
|
||
" <th>community</th>\n",
|
||
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||
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|
||
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|
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||
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|
||
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|
||
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|
||
" <td>ЖК «AVrorA»,</td>\n",
|
||
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|
||
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|
||
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|
||
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|
||
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||
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|
||
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||
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|
||
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|
||
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|
||
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||
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||
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|
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|
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|
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|
||
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|
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|
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|
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|
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" <tr>\n",
|
||
" <th>299</th>\n",
|
||
" <td>45.0</td>\n",
|
||
" <td>1</td>\n",
|
||
" <td>-144</td>\n",
|
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" <td>ЖК «Спортивный парк»</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>302</th>\n",
|
||
" <td>48.0</td>\n",
|
||
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|
||
" <td>-144</td>\n",
|
||
" <td>ЖК «Спортивный парк»</td>\n",
|
||
" </tr>\n",
|
||
" <tr>\n",
|
||
" <th>304</th>\n",
|
||
" <td>51.0</td>\n",
|
||
" <td>1</td>\n",
|
||
" <td>-144</td>\n",
|
||
" <td>ЖК «Спортивный парк»</td>\n",
|
||
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|
||
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|
||
"</table>\n",
|
||
"<p>125 rows × 4 columns</p>\n",
|
||
"</div>\n",
|
||
" <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-59e1f278-9963-4337-84a0-ebbd7c6b99c8')\"\n",
|
||
" title=\"Convert this dataframe to an interactive table.\"\n",
|
||
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|
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|
||
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|
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|
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|
||
" \n",
|
||
" <style>\n",
|
||
" .colab-df-container {\n",
|
||
" display:flex;\n",
|
||
" flex-wrap:wrap;\n",
|
||
" gap: 12px;\n",
|
||
" }\n",
|
||
"\n",
|
||
" .colab-df-convert {\n",
|
||
" background-color: #E8F0FE;\n",
|
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" border: none;\n",
|
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|
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|
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|
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|
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|
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" padding: 0 0 0 0;\n",
|
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" width: 32px;\n",
|
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" }\n",
|
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"\n",
|
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" .colab-df-convert:hover {\n",
|
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" background-color: #E2EBFA;\n",
|
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" box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
|
||
" fill: #174EA6;\n",
|
||
" }\n",
|
||
"\n",
|
||
" [theme=dark] .colab-df-convert {\n",
|
||
" background-color: #3B4455;\n",
|
||
" fill: #D2E3FC;\n",
|
||
" }\n",
|
||
"\n",
|
||
" [theme=dark] .colab-df-convert:hover {\n",
|
||
" background-color: #434B5C;\n",
|
||
" box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
|
||
" filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
|
||
" fill: #FFFFFF;\n",
|
||
" }\n",
|
||
" </style>\n",
|
||
"\n",
|
||
" <script>\n",
|
||
" const buttonEl =\n",
|
||
" document.querySelector('#df-59e1f278-9963-4337-84a0-ebbd7c6b99c8 button.colab-df-convert');\n",
|
||
" buttonEl.style.display =\n",
|
||
" google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
|
||
"\n",
|
||
" async function convertToInteractive(key) {\n",
|
||
" const element = document.querySelector('#df-59e1f278-9963-4337-84a0-ebbd7c6b99c8');\n",
|
||
" const dataTable =\n",
|
||
" await google.colab.kernel.invokeFunction('convertToInteractive',\n",
|
||
" [key], {});\n",
|
||
" if (!dataTable) return;\n",
|
||
"\n",
|
||
" const docLinkHtml = 'Like what you see? Visit the ' +\n",
|
||
" '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
|
||
" + ' to learn more about interactive tables.';\n",
|
||
" element.innerHTML = '';\n",
|
||
" dataTable['output_type'] = 'display_data';\n",
|
||
" await google.colab.output.renderOutput(dataTable, element);\n",
|
||
" const docLink = document.createElement('div');\n",
|
||
" docLink.innerHTML = docLinkHtml;\n",
|
||
" element.appendChild(docLink);\n",
|
||
" }\n",
|
||
" </script>\n",
|
||
" </div>\n",
|
||
" </div>\n",
|
||
" "
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"execution_count": 442
|
||
}
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"source": [
|
||
"df = pd.read_csv('/content/drive/MyDrive/krasnodar_hack/krasnodar_data_final.csv')\n",
|
||
"df = df[~df['area'].isna()]\n",
|
||
"df = df[~df['done_date'].isna()]"
|
||
],
|
||
"metadata": {
|
||
"id": "FUArSeHiUQk9"
|
||
},
|
||
"execution_count": null,
|
||
"outputs": []
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"source": [
|
||
"df = df.loc[df.drop(columns=['price_per_m', 'date', 'floor', 'done_date']).drop_duplicates().index]"
|
||
],
|
||
"metadata": {
|
||
"id": "F93iEA_4UUmS"
|
||
},
|
||
"execution_count": null,
|
||
"outputs": []
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"source": [
|
||
"rep = {\n",
|
||
" '4 квартал 2021' : \"4 квартал 2021\",\n",
|
||
" '9 квартал 2021' : \"4 квартал 2021\",\n",
|
||
" '8 квартал 2021' : \"4 квартал 2021\",\n",
|
||
" '7 квартал 2021' : \"4 квартал 2021\",\n",
|
||
" '6 квартал 2021' : \"4 квартал 2021\",\n",
|
||
" '5 квартал 2021' : \"4 квартал 2021\",\n",
|
||
" '18 квартал 2021' : \"4 квартал 2021\",\n",
|
||
" '17 квартал 2021' : \"4 квартал 2021\",\n",
|
||
" '16 квартал 2021' : \"4 квартал 2021\",\n",
|
||
" '14 квартал 2021' : \"4 квартал 2021\",\n",
|
||
" '13 квартал 2021' : \"4 квартал 2021\",\n",
|
||
" '12 квартал 2021' : \"4 квартал 2021\",\n",
|
||
" '11 квартал 2021' : \"4 квартал 2021\",\n",
|
||
" '10 квартал 2021' : \"4 квартал 2021\",\n",
|
||
" '15 квартал 2021' : \"4 квартал 2021\",\n",
|
||
"}\n",
|
||
"kvartal_to_date = {\n",
|
||
" '4 квартал 2021': '2021-12-15',\n",
|
||
" '2 квартал 2022': '2022-06-15',\n",
|
||
" '3 квартал 2022': '2022-09-15',\n",
|
||
" '1 квартал 2023': '2023-03-15',\n",
|
||
" '2 квартал 2023': '2023-06-15',\n",
|
||
" '3 квартал 2023': '2023-09-15',\n",
|
||
" '4 квартал 2023': '2023-12-15',\n",
|
||
" '1 квартал 2024': '2024-03-15',\n",
|
||
" '2 квартал 2024': '2024-06-15',\n",
|
||
" '3 квартал 2024': '2024-09-15',\n",
|
||
" '4 квартал 2024': '2024-12-15',\n",
|
||
" 'дом сдан': '1990-12-15'\n",
|
||
"}\n",
|
||
"\n",
|
||
"df['done_date'] = df['done_date'].replace(rep).replace(kvartal_to_date)\n",
|
||
"\n",
|
||
"df.done_date = pd.to_datetime(df.done_date)\n",
|
||
"df.date = pd.to_datetime(df.date)\n",
|
||
"df['days_to_done'] = df['done_date'] - df['date']\n",
|
||
"df['days_to_done'] = df['days_to_done'].apply(lambda x: int(str(x).split()[0]))\n",
|
||
"\n",
|
||
"df = df[df['days_to_done'] > -2000]"
|
||
],
|
||
"metadata": {
|
||
"id": "YGVXldFpVK3r"
|
||
},
|
||
"execution_count": null,
|
||
"outputs": []
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"source": [
|
||
"import uuid"
|
||
],
|
||
"metadata": {
|
||
"id": "YQ8yCNOUWfBH"
|
||
},
|
||
"execution_count": null,
|
||
"outputs": []
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"source": [
|
||
"df['id'] = [uuid.uuid4() for i in range(df.shape[0])]"
|
||
],
|
||
"metadata": {
|
||
"colab": {
|
||
"base_uri": "https://localhost:8080/"
|
||
},
|
||
"id": "uqaytktLVtEG",
|
||
"outputId": "4987f94a-12b5-4054-e04f-6614ddaa2f87"
|
||
},
|
||
"execution_count": null,
|
||
"outputs": [
|
||
{
|
||
"output_type": "stream",
|
||
"name": "stderr",
|
||
"text": [
|
||
"<ipython-input-460-5bf864eb9863>:1: SettingWithCopyWarning: \n",
|
||
"A value is trying to be set on a copy of a slice from a DataFrame.\n",
|
||
"Try using .loc[row_indexer,col_indexer] = value instead\n",
|
||
"\n",
|
||
"See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
|
||
" df['id'] = [uuid.uuid4() for i in range(df.shape[0])]\n"
|
||
]
|
||
}
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"source": [
|
||
"df = df.reset_index(drop=True)"
|
||
],
|
||
"metadata": {
|
||
"id": "PEhBbLi2WAjW"
|
||
},
|
||
"execution_count": null,
|
||
"outputs": []
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"source": [
|
||
"df['community'] = df['community'].apply(lambda x: x.strip())"
|
||
],
|
||
"metadata": {
|
||
"id": "OzeORCqdYs-i"
|
||
},
|
||
"execution_count": null,
|
||
"outputs": []
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"source": [
|
||
"model.predict(pd.DataFrame({'area': [58.0], 'num_beds': [1], 'days_to_done': [1000], 'community':['ЖК «Novella»']}))"
|
||
],
|
||
"metadata": {
|
||
"colab": {
|
||
"base_uri": "https://localhost:8080/"
|
||
},
|
||
"id": "3Y17izGaXrBB",
|
||
"outputId": "98da920d-41b9-4b94-cab9-e47b969f39f0"
|
||
},
|
||
"execution_count": null,
|
||
"outputs": [
|
||
{
|
||
"output_type": "execute_result",
|
||
"data": {
|
||
"text/plain": [
|
||
"array([135260.44035891])"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"execution_count": 499
|
||
}
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"source": [
|
||
"df.to_csv('info_final.csv', index=False)"
|
||
],
|
||
"metadata": {
|
||
"id": "fVwsL2LcX9g6"
|
||
},
|
||
"execution_count": null,
|
||
"outputs": []
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"source": [
|
||
"import tqdm\n",
|
||
"\n",
|
||
"preds = []\n",
|
||
"for idx, row in tqdm.tqdm(df.iterrows()):\n",
|
||
" subjs = {\n",
|
||
" 'id':str(row['id']),\n",
|
||
" 'days_to_done':row['days_to_done'],\n",
|
||
" 'price_at_moment': round(model.predict(row[model.feature_names_])*row.area)\n",
|
||
" }\n",
|
||
" \n",
|
||
" prices_after = []\n",
|
||
" for days in [1000, 700, 500, 300, 200, 100, 50, 0, -50, -100]:\n",
|
||
" row['days_to_done'] = days\n",
|
||
" pred = round(model.predict(row[model.feature_names_])*row.area)\n",
|
||
" \n",
|
||
" subjs[f'{days}_to_done'] = pred\n",
|
||
"\n",
|
||
" if days < subjs['days_to_done']:\n",
|
||
" prices_after.append(pred)\n",
|
||
"\n",
|
||
" if max(prices_after) == pred:\n",
|
||
" subjs['max_price_after_invest'] = pred\n",
|
||
" subjs['increase'] = subjs['max_price_after_invest'] - subjs['price_at_moment']\n",
|
||
" subjs['increase_procent'] = round((subjs['increase']/(subjs['price_at_moment']*100))*100*100, 2)\n",
|
||
"\n",
|
||
" subjs['days_for_increase'] = subjs['days_to_done']-days\n",
|
||
"\n",
|
||
" if subjs['days_for_increase'] >= 365:\n",
|
||
" subjs['status'] = 'long'\n",
|
||
" else:\n",
|
||
" subjs['status'] = 'short'\n",
|
||
"\n",
|
||
" \n",
|
||
" if subjs['increase_procent'] >= 7:\n",
|
||
" subjs['risk'] = 'good'\n",
|
||
" elif subjs['increase_procent'] <= 0:\n",
|
||
" subjs['risk'] = 'bad'\n",
|
||
" else:\n",
|
||
" subjs['risk'] = 'riskey'\n",
|
||
"\n",
|
||
"\n",
|
||
" preds.append(subjs)"
|
||
],
|
||
"metadata": {
|
||
"colab": {
|
||
"base_uri": "https://localhost:8080/"
|
||
},
|
||
"id": "OVh5D4kQX-0J",
|
||
"outputId": "6ec9394b-4554-4c0d-d40a-8e696c56b71b"
|
||
},
|
||
"execution_count": null,
|
||
"outputs": [
|
||
{
|
||
"output_type": "stream",
|
||
"name": "stderr",
|
||
"text": [
|
||
"153it [00:03, 44.60it/s]\n"
|
||
]
|
||
}
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"source": [
|
||
"f = pd.DataFrame(preds)"
|
||
],
|
||
"metadata": {
|
||
"id": "f5ifJU9habaL"
|
||
},
|
||
"execution_count": null,
|
||
"outputs": []
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"source": [
|
||
"f.risk.value_counts()"
|
||
],
|
||
"metadata": {
|
||
"colab": {
|
||
"base_uri": "https://localhost:8080/"
|
||
},
|
||
"id": "2GJK98WAmwRj",
|
||
"outputId": "5ca8d789-1afe-4051-f611-bf62eea3bdfb"
|
||
},
|
||
"execution_count": null,
|
||
"outputs": [
|
||
{
|
||
"output_type": "execute_result",
|
||
"data": {
|
||
"text/plain": [
|
||
"good 67\n",
|
||
"riskey 47\n",
|
||
"bad 39\n",
|
||
"Name: risk, dtype: int64"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"execution_count": 587
|
||
}
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"source": [
|
||
"df.id = df.id.astype(str)"
|
||
],
|
||
"metadata": {
|
||
"id": "1YzsbENSnDPx"
|
||
},
|
||
"execution_count": null,
|
||
"outputs": []
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"source": [
|
||
"f.merge(df[['community', 'liter_num', 'done_date', 'developer', 'floor', 'area',\n",
|
||
" 'num_beds', 'days_to_done', 'id']], on='id', how='left').to_csv('merged_stats_final.csv', index=False)"
|
||
],
|
||
"metadata": {
|
||
"id": "_7FnZAE_m-Bt"
|
||
},
|
||
"execution_count": null,
|
||
"outputs": []
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"source": [
|
||
"import json\n",
|
||
"\n",
|
||
"with open('stats_final.json', 'w') as f:\n",
|
||
" json.dump(preds, f)"
|
||
],
|
||
"metadata": {
|
||
"id": "oHMm4LIKb02q"
|
||
},
|
||
"execution_count": null,
|
||
"outputs": []
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"source": [
|
||
"cols = ['1000_to_done', '700_to_done',\n",
|
||
" '500_to_done', '300_to_done', '200_to_done',\n",
|
||
" '100_to_done', '50_to_done', '0_to_done', '-50_to_done',\n",
|
||
" '-100_to_done']"
|
||
],
|
||
"metadata": {
|
||
"id": "BfqXQdrSiN6_"
|
||
},
|
||
"execution_count": null,
|
||
"outputs": []
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"source": [
|
||
"import plotly.express as px"
|
||
],
|
||
"metadata": {
|
||
"id": "lDjZh9gVjCvu"
|
||
},
|
||
"execution_count": null,
|
||
"outputs": []
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"source": [
|
||
"t = f[f['increase_procent'] > 15][cols].T"
|
||
],
|
||
"metadata": {
|
||
"id": "eY5RJbHIeWvY"
|
||
},
|
||
"execution_count": null,
|
||
"outputs": []
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"source": [
|
||
"fig = px.line(t)\n",
|
||
"fig.show()"
|
||
],
|
||
"metadata": {
|
||
"colab": {
|
||
"base_uri": "https://localhost:8080/",
|
||
"height": 542
|
||
},
|
||
"id": "4mg4YGkWiivI",
|
||
"outputId": "314a02a7-1a9c-4fe4-b4d5-3d5b73a24ac0"
|
||
},
|
||
"execution_count": null,
|
||
"outputs": [
|
||
{
|
||
"output_type": "display_data",
|
||
"data": {
|
||
"text/html": [
|
||
"<html>\n",
|
||
"<head><meta charset=\"utf-8\" /></head>\n",
|
||
"<body>\n",
|
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" <div> <script src=\"https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js?config=TeX-AMS-MML_SVG\"></script><script type=\"text/javascript\">if (window.MathJax) {MathJax.Hub.Config({SVG: {font: \"STIX-Web\"}});}</script> <script type=\"text/javascript\">window.PlotlyConfig = {MathJaxConfig: 'local'};</script>\n",
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