ML-SVR_ad.py.ipynb

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      "Requirement already satisfied: fredapi in c:\\users\\dadda\\anaconda3\\envs\\pythondata\\lib\\site-packages (0.5.0)\n",
      "Requirement already satisfied: pandas in c:\\users\\dadda\\appdata\\roaming\\python\\python37\\site-packages (from fredapi) (1.3.5)\n",
      "Requirement already satisfied: numpy>=1.17.3 in c:\\users\\dadda\\appdata\\roaming\\python\\python37\\site-packages (from pandas->fredapi) (1.21.5)\n",
      "Requirement already satisfied: python-dateutil>=2.7.3 in c:\\users\\dadda\\appdata\\roaming\\python\\python37\\site-packages (from pandas->fredapi) (2.8.2)\n",
      "Requirement already satisfied: pytz>=2017.3 in c:\\users\\dadda\\appdata\\roaming\\python\\python37\\site-packages (from pandas->fredapi) (2021.3)\n",
      "Requirement already satisfied: six>=1.5 in c:\\users\\dadda\\anaconda3\\envs\\pythondata\\lib\\site-packages (from python-dateutil>=2.7.3->pandas->fredapi) (1.15.0)\n",
      "Note: you may need to restart the kernel to use updated packages.\n"
     ]
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   ],
   "source": [
    "pip install fredapi"
   ]
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  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "5d602a27",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from fredapi import Fred\n",
    "from AD_CONFIG import fred_key\n",
    "from functools import reduce\n",
    "fred = Fred(api_key=fred_key)\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "id": "3756f794",
   "metadata": {},
   "outputs": [],
   "source": [
    "# fred_indexes = [\"EMRATIO\",\"UNEMPLOY\", 'JTSJOL', 'JTS3000JOL', 'JTS6000JOL',\n",
    "# 'JTS9000JOL', 'CUSR0000SAF112','JTU5100JOL','JTU5200JOL']\n",
    "\n",
    "fred_indexes = [\"UNRATE\", \"M2SL\",\"M1SL\", \"CSUSHPINSA\",\n",
    "\"CIVPART\",\"PSAVERT\",\"CES0500000003\", \"LNS14000006\", \"LNS14000001\"]\n",
    "#fred_indexes = [\"UNRATE\",\"FEDFUNDS\",\"CPIAUCSL\",\"INTDSRUSM193N\",\"T10YIEM\",\"TB3MS\", \"CPALTT01USM657N\",\n",
    "#\"CIVPART\",\"PSAVERT\",\"MPRIME\", \"LNS14000006\"]"
   ]
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   "source": [
    "\n",
    "### we used a python library call fred api to query the API endpoint and create dataframes\n",
    "dataframes = []\n",
    "### loop thru all the different indexes and create a dataframe for each \n",
    "for ind in fred_indexes:\n",
    "    df = pd.DataFrame(fred.get_series(ind, observation_start='2000-1-1'))\n",
    "    df.index.names = ['date']\n",
    "    dataframes.append(df)\n",
    "\n",
    "counter = 0\n",
    "for dataframe in dataframes:\n",
    "   counter = counter + 1"
   ]
  },
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   "execution_count": 100,
   "id": "84c991df",
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     "text": [
      "C:\\Users\\dadda\\AppData\\Roaming\\Python\\Python37\\site-packages\\ipykernel_launcher.py:2: FutureWarning:\n",
      "\n",
      "Passing 'suffixes' which cause duplicate columns {'0_x'} in the result is deprecated and will raise a MergeError in a future version.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "### merge/join the dataframes on the date \n",
    "combined = reduce(lambda x, y: pd.merge(x, y, on='date'), dataframes)\n",
    "combined.columns = fred_indexes\n",
    "combined.to_csv(\"Resources/combine.csv\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "id": "f9c6f0a7",
   "metadata": {},
   "outputs": [],
   "source": [
    "### SVR model\n",
    "df = combined"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "id": "fc796d41",
   "metadata": {},
   "outputs": [],
   "source": [
    "# use pandas library to randomly sample data from the dataset\n",
    "## sklean has \"test train split\", this function randomly samples the data and splits into 2 groups \n",
    "## a treatment group and a control group or a training group for the model and a test group for the model\n",
    "from sklearn.model_selection import train_test_split\n",
    "train, test = train_test_split(df, test_size = 0.3)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "id": "41537119",
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   "outputs": [],
   "source": [
    "\n",
    "# we remove the unemployment because we are trying to predict it \n",
    "x_train = train.drop('UNRATE', axis=1)\n",
    "y_train = train['UNRATE']\n",
    "\n",
    "x_test = test.drop('UNRATE', axis = 1)\n",
    "y_test = test['UNRATE']\n"
   ]
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   "cell_type": "code",
   "execution_count": 104,
   "id": "579c4f36",
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   "source": [
    "# scale all the date which is all numerical values (no categorical) to values between zero and one\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "scaler = MinMaxScaler(feature_range=(0, 1))\n",
    "\n",
    "x_train_scaled = scaler.fit_transform(x_train)\n",
    "x_train = pd.DataFrame(x_train_scaled)\n",
    "\n",
    "x_test_scaled = scaler.fit_transform(x_test)\n",
    "x_test = pd.DataFrame(x_test_scaled)\n",
    "\n",
    "\n",
    "from sklearn.svm import SVR\n",
    "# most important SVR parameter is Kernel type. \n",
    "# It can be #linear,polynomial or gaussian SVR. We have a non-linear condition \n",
    "# #so we can select polynomial or gaussian but here we select RBF(a #gaussian type) kernel."
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   "cell_type": "code",
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   "id": "4bce974f",
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      "model score\n",
      "0.944707528875783\n"
     ]
    }
   ],
   "source": [
    "regressor = SVR(kernel='rbf')\n",
    "regressor.fit(x_train, y_train)\n",
    "y_pred = regressor.predict(x_test)\n",
    "\n",
    "score=regressor.score(x_train,y_train)\n",
    "print(\"model score\")\n",
    "print(score)"
   ]
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  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "bda1a351",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Visualizations\n",
    "import plotly.graph_objects as go # for data visualization\n",
    "import plotly.express as px # for data visualization\n",
    "from plotly.subplots import make_subplots\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "12921d37",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df.rename(columns = {\"LNS14000006\":\"Black or African American\"})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "8ca35e71",
   "metadata": {},
   "outputs": [],
   "source": [
    "# # Create a scatter plot\n",
    "\n",
    "fig = px.scatter(df, x=df['Black or African American'], y=df['UNRATE'], \n",
    "opacity=0.8, color_discrete_sequence=['black'])"
   ]
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