mlessentials/Lab06/Exercise6.02/Exercise6_02.ipynb

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    "# import libraries \n",
    "\n",
    "import pandas as pd \n",
    "\n",
    "from sklearn.model_selection import train_test_split \n",
    "\n",
    "from sklearn.linear_model import LinearRegression "
   ]
  },
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    "# column headers \n",
    "\n",
    "_headers = ['CIC0', 'SM1', 'GATS1i', 'NdsCH', 'Ndssc', 'MLOGP', 'response'] \n",
    "\n",
    "# read in data \n",
    "\n",
    "df = pd.read_csv('../Dataset/qsar_fish_toxicity.csv', names=_headers, sep=';') "
   ]
  },
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    "df.head()"
   ]
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    "# Let's split our data \n",
    "\n",
    "features = df.drop('response', axis=1).values \n",
    "\n",
    "labels = df[['response']].values \n",
    "\n",
    " \n",
    "\n",
    "X_train, X_eval, y_train, y_eval = train_test_split(features, labels, test_size=0.2, random_state=0) \n",
    "\n",
    "X_val, X_test, y_val, y_test = train_test_split(X_eval, y_eval, random_state=0) "
   ]
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    "model = LinearRegression() "
   ]
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   "source": [
    "model.fit(X_train, y_train) "
   ]
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    "y_pred = model.predict(X_val) "
   ]
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   "source": [
    "r2 = model.score(X_val, y_val) \n",
    "\n",
    "print('R^2 score: {}'.format(r2)) "
   ]
  },
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   "source": [
    "_ys = pd.DataFrame(dict(actuals=y_val.reshape(-1), predicted=y_pred.reshape(-1))) \n",
    "\n",
    "_ys.head() "
   ]
  }
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